data handling problem solving

Data Handling

Data handling is considered one of the most important topics in statistics as it deals with collecting sets of data, maintaining security, and the preservation of the research data. The data here is a set of numbers that help in analyzing that particular set or sets of data. Data handling can be represented visually in the form of graphs. Let us learn more about this interesting concept, the different graphs used, and solve a few examples for better understanding.

Definition of Data Handling

Data Handling is the process of gathering, recording, and presenting information in a way that is helpful to analyze, make predictions and choices. Anything that can be grouped based on certain comparable parameters can be thought of as data . Parameters mean the context in which the comparison is made between the objects. Data handling usually represent in the form of pictographs, bar graphs, pie charts, histograms , line graphs, stem and leaf plots , etc. All of them have a different purpose to serve. Have a look at the composition of the air that we have learned about in our science classes.

The constituents of air are presented with different colors in the form of parts of a pie. Do you think, a bar chart, line graph, or any other graphical representation would be able to communicate the information as effectively as this one. Definitely no. With a detailed study of each of them, you can clearly understand the purpose of each of them and use them suitably.

Types of Data

Data handling is performed depending on the types of data. Data is classified into two types, such as Quantitative Data and Qualitative Data. Quantitive data gives numerical information, while qualitative data gives descriptive information about anything. Quantitative can be either discrete or continuous data.

Important Terms in Data Handling

In data handling, there are 4 important terms or most frequently used terms that make it simple to understand the concept better. The terms are:

• Data: It is the collection of numerical figures of any kind of information
• Raw Data: The observation gathered initially is called the raw data.
• Range: It is the difference between the highest and lowest values in the data collection.
• Statistics : It deals with the collection, representation, analysis, and interpretation of numerical data.

Steps Involved in Data Handling

Following are the steps to follow in data handling:

Graphical Representation of Data Handling

Data handling can be represented in a number of graphical ways. Here is a list of various types of graphical representations of data that are very effective in data handling.

Bar graphs represent data in the form of vertical or horizontal bars showing data with rectangular bars and the heights of bars are proportional to the values that they represent. Bar graphs help in the comparison of data and this type of graph is most widely used in statistics. Look at the image below as an example.

Pictographs or Picture Graphs

Pictograph is a type of graph where information is represented in the form of pictures, icons, or symbols. It is the simplest form of representing data in statistics and data handling. Since the use of images and symbols are more in a pictograph, interpreting data is made easy along with representing a large number of data. Look at the example below for a better understanding.

Line Graphs

In data handling the data represented in the form of a line on a graph is the line graph . The graph helps in showcasing the different trends or changes in the data. The line segment plotted on the graph is constructed by connecting individual data points together. Look at the example below to understand it better.

A pie chart is data represented in a circular graph divided into smaller sectors to denote certain information. Pie charts help in showcasing the profit and loss for a business, while in school in showcasing the number depending on the data. This kind of chart is widely used in marketing sales. Look at the example below, the pie chart shows how people like the mentioned fruits from a group of 360.

Scatter Plot

Scatter plot represents the points and then the best fit line is drawn through some of the points. Any 3D data in data handling can be represented by a scatter plot. Look at the example below to understand it better.

Related Topic

Listed below are a few interesting topics related to data handling. Take a look.

• Absolute Value Graph
• Frequency Distribution Table
• Probability and Statistics

Examples on Data Handling

Example 1: Henry wants to introduce his 5-year-old daughter to data handling. Which type of graphical representation can he use for this?

As his daughter is just 5 years old, he should prefer using Pictograph to introduce data handling. In this representation, simple pictures like circles, stars are drawn to represent different data.

Example 2: How is data represented graphically?

Solution: Various types of graphs that can be used for representing data are:

• Scatter plot
• Pie chart/ Circle chart
• Picture graph

Depending on the purpose, a suitable graph can be chosen.

Example 3: Here is a review of an electronic product. Out of all the people who gave their reviews, 16 of them gave a 5-star rating to the product. Can you find out how many people provided their feedback in all?

Let the total reviews be x.

Number of people who gave 5 star = 16

Percentage of people who gave 5 star = 64%

So, number of people who gave 5 star = 64 % × x

16 = 64/100 × x

x = (16 × 64)/100

Therefore, 25 people gave reviews for the product

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Practice Questions on Data Handling

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FAQs on Data Handling

What is data handling.

Data Handling is the process of gathering, recording, and presenting information in a way that is helpful to analyze, make predictions and choices. There are two types of data handling namely quantitative data and qualitative data. Data handling can be represented through various graphs.

What are the Two Types of Data Handling?

The two types of data handling are qualitative data and quantitative data. Quantitive data gives numerical information, while qualitative data gives descriptive information about anything. Quantitative can be either discrete or continuous data.

What are the Steps Involved in Data Handling?

The six steps that are involved in data handling are:

• Collection of Data
• Presentation of Data
• Graphical Representation of Data
• Analyzing the Data

What are the Types of Graphical Representations in Data Handling?

There are numerous types of graphical representation for the data that are available. Some of the most extensively used graphical representations are :

What is the Difference Between Data and Information in Data Handling?

The term data refers to the collection of certain facts that are quantitive in nature like height, number of children, etc. Information on the other hand is a form of data after being processed, arranged, and presented in a form that gives meaning to the data.

What is the Difference Between the Chart and Graph?

The difference between chart and graph can be understood from the fact that - All graphs are charts but every chart is not a graph. Charts display data in the form of a diagram, table, or graph. So, the graph is just a pictorial way of presentation of information.

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Course: Class 6   >   Unit 9

• Intro to data handling
• Solving problems with picture graphs

Data handling 9.1

• Your answer should be
• an integer, like 6 ‍  
• an exact decimal, like 0.75 ‍  
• a simplified proper fraction, like 3 / 5 ‍  
• a simplified improper fraction, like 7 / 4 ‍  
• a mixed number, like 1   3 / 4 ‍  

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Published 2011 Revised 2017

Enriching Data Handling

Data handling is one of the central activities in which real mathematicians engage: they are frequently analysing data that they have gathered in various contexts and looking for patterns and generalities within them. In schools we often undertake tasks in which we encourage children to collect data about themselves and their friends but the emphasis tends to be on presenting data in a variety of forms such as bar charts or pictograms. Analysis is often confined to identifying the most popular or least popular item. These limitations tend to restrict the interest and variety of the contexts that are explored, and fail to engage children in any significant mathematical thinking. Here are some alternative suggestions taken from the NRICH website that offer a broader view of data and ask some tricky questions about it. Let us start with a simple question which would be suitable for a group of children to tackle at Stage 1: Ladybird Count .

This question offers children some raw data that they have not had to collect themselves. This has some advantages: the teacher knows that everyone has the same information without worrying about the accuracy of their recording methods. So, we have a data handling problem that focuses on analysis rather than collection. Now the children need to begin to make sense of the situation. It is probably helpful to ask them to think about the pictures and to talk to each other about what the problem means. Offer them plenty of opportunities to think without insisting on quick answers. After they have had this chance, find out their ideas and, if need be, they can be encouraged to focus by asking them: "How many ladybirds does each child have?"

From this point the question concentrates on how the data could be represented to show how many ladybirds the different children have. Be prepared to consider a variety of responses: the solutions do not need to be bar charts or pictograms. Their suggestions will provide insight into the children's own methods of recording. Engaging in conversation with them about their representation may be essential and is a great way to probe their previous experiences of handling and recording data. On the website we post children's solutions to our problems and we received an interesting mindmap from one pupil as a way of representing the data. The Pet Graph is a question that has the representation of the data done already.

This time the question is asking the children to work out how the graph should be labelled. This type of task is often regarded as being trivial by teachers and yet it involves crucial aspects of data handling. To be successful pupils need to understand what the graph is saying and relating that to the information they have been given. This requires high level thinking, especially if you ask your pupils to justify what they have done by asking questions such as: "How do you know that ...?" "Why can't the yellow bar represent ...?" "Which bar was the easiest to identify and why?" Later on in secondary school children often leave out the labels on axes, rendering the representation meaningless. This question would help children to realise the significance of the labels. It also involves working with unknown quantities that are the precursor to algebra. Once again the key approach involves discussion and thought, and children should be encouraged to think about the question and talk about it in pairs before a class discussion. Slow Coach is another question with very different data, not statistical this time, and appropriate for use with children at Stage 2.

Timetables are tricky things and they sometimes need careful thinking to make sense of them. As adults we often forget how many conventions are involved in their presentation and we need to help children to unpack the meanings in them. Once again the role of thinking quietly and discussing the meanings in pairs before voicing suggestions to a wider audience is vital. The teacher may find it helpful to ask children about what is going on and how they might represent the information in different ways. How about some pictures of the buses on the road from A to B? At 0600 there are just two buses starting off but what about at 0620, 0640, 0700 and so on? What understanding do the children have of the representations of time on the 24 hour clock? The hint to the problems suggests that the children should: "Draw a diagram of all the coaches on the road when this one sets off." Once again there is a solution from children posted on the website and they have explained their reasoning as well as given the answer: "The 10am coach will see the 8.40, 9.00, 9.20, 9.40, 10, 10.20, 10.40, 11, 11.20 coaches on its way to Betaville. Therefore it sees 9 coaches. We know this because they are travelling at the same speed and on the same path." Now for another question in a totally different context appropriate for children at Stage 2. Family Tree is a very challenging problem and should stretch even the highest attainers in the class. At the same time it is an engaging scenario and should provoke some good discussion. This time the data is in the form of a description.

So what exactly do we need to do? The problem is asking us to fill in the letters of all the family members on the family tree. The clues in the question are sufficient to do so. How could we encourage children to start working on a solution? Once again encouragement to think quietly, make jottings and discuss things with a partner before embarking on class discussion will help to raise the quality of mathematical thinking and reasoning. One of the pitfalls is the tendency to think of the mathematicians as male which obviously addresses issues of gender stereotyping. Trying a similar problem with a group of children recently provoked so much enthusiastic participation that they found it hard to wait for a turn to write on the board. The kind of logical reasoning that this requires is central to mathematical thinking and reasoning and yet we tend to offer few opportunities to tackle logical puzzles like this in school mathematics. The data handling involved in this problem is easy to relate to and the problem-setting engaging. These problems offer interesting contexts in which to explore data handling and there are plenty more on offer on NRICH from football results to codes.

This article first appeared in Maths Coordinator's File issue 19, published by pfp publishing. 

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Data Handling Worksheets

Data handling is considered to be one of the most important topics in statistics. It is the process of gathering, record ing, and presenting information in a way that makes it easier to analyze information. As this topic is present across multiple grades, students need to practice questions from data handling worksheets and master the fundamental concepts at an early stage. ...Read More Read Less

Benefits of Data Handling Worksheets

Data handling is a vast topic that is present in almost all elementary and junior high from grades 1 to grade 8 syllabus, indicating the importance of the topic itself. Hence, students need to make sure that they are thorough with the data handling concepts that they study in each grade. The best way to make sure that students understand each concept is to solve data handling worksheets available on BYJU’S Math.

Here are some of the ways in which the worksheets on data handling can benefit a student:

Improves analytical skills:

The analysis of data plays a key role in the subject of data handling. Solving questions based on the analysis of data greatly improves a student’s analytical skills, which will help them in many real-life scenarios.

Improves logical decision making skills:

The analysis of data plays which is a key skill that develops as part of data handling further lends itself to make logical and informed decisions. 

Facilitates time management:

It is common for students to perform below par in math due to inefficient time management. Students can alleviate this issue by practicing questions from worksheets and time their attempts. 

Improves problem-solving skills:

By practicing math assignments, children can improve their problem-solving abilities. As a result, they become better logical thinkers and can come up with creative solutions to a wide variety of problems.

• Interactive Worksheets
• Printable Worksheets

Choose Math Worksheets by Grade

Choose math worksheets by topic.

Data handling consists of different steps like data collection, data presentation, graphical representation, analysis and conclusion. Students in the lower grades start learning the basic concepts of data handling. As students progress to higher grades, they learn about the same data handling concepts but in much more detail. To make sure that students stay in sync with the data handling concepts taught in each grade, they can solve free printable and online data handling worksheets available on the BYJU’S Math website. 

If you feel the need to refresh your understanding of data handling as a concept, click on the following links:

• Data and Survey
• Mean of Data
• Median of Data
• Mode of data
• Arranging data
• Drawing and Distribution of Data
• Data Display
• Explain Data and Interpret Data By Tally Chart
• Interpret Data by Picture Graphs and Bar Graphs

Printable and Online Data Handling Worksheets

BYJU’S Math caters to the learning style of each and every student. For instance, students have the option to opt for either online or offline worksheets. Students who prefer online worksheets can select interactive worksheets, while students who prefer pen-and-paper worksheets can download free printable worksheets.

How is data handling learned through data handling worksheets?

Data handling is the process of obtaining, recording, and presenting information so that it can be analyzed, predicted, and chosen in an effective manner. It is an important branch of mathematics. The data handling worksheets cover questions around all these processes and introduce various formulas depending on the grade it is intended for.

Which grades are these data handling worksheets part of?

Data handling is a vast topic that deals with the collection, presentation, graphing, analysis, and interpretation of data. Students have a lot to learn at each step of this process. Hence, the topics in data handling worksheets can vary from grade 1 to 8 grade.

Do these data handling worksheets help students understand the importance of data handling in daily life?

Many decisions that we make in our daily life take data or prior knowledge into account. For example, a high school student checks university rankings, placement statistics, acceptance rates, and so on, before applying for admission to a university. In other words, the student studies data before making decisions. This is just one of the applications of data in real life.  Many of the questions in the data handling worksheets revolve around these real life problem scenarios.

What is the difference between printable and online data handling worksheets?

Printable worksheets are worksheets that students can download and print for free. On the other hand, interactive or online data handling worksheets are worksheets that students can solve online. Students can choose between printable worksheets and online worksheets depending on their preferences.

What are the benefits of solving questions in the data handling worksheets?

Solving questions in the data handling worksheets help students improve their ability to analyze and represent data in an efficient manner. Students will be able to practice different methods to arrange and graph data by solving questions based on these concepts.

Data Analytics with R

1 problem solving with data, 1.1 introduction.

This chapter will introduce you to a general approach to solving problems and answering questions using data. Throughout the rest of the module, we will reference back to this chapter as you work your way through your own data analysis exercises.

The approach is applicable to actuaries, data scientists, general data analysts, or anyone who intends to critically analyze data and develop insights from data.

This framework, which some may refer to as The Data Science Process includes the following five main components:

• Data Collection
• Data Cleaning
• Exploratory Data Analysis
• Model Building
• Inference and Communication

Note that all five steps may not be applicable in every situation, but these steps should guide you as you think about how to approach each analysis you perform.

In the subsections below, we’ll dive into each of these in more detail.

1.2 Data Collection

In order to solve a problem or answer a question using data, it seems obvious that you must need some sort of data to start with. Obtaining data may come in the form of pre-existing or generating new data (think surveys). As an actuary, your data will often come from pre-existing sources within your company. This could include querying data from databases or APIs, being sent excel files, text files, etc. You may also find supplemental data online to assist you with your project.

For example, let’s say you work for a health insurance company and you are interested in determining the average drive time for your insured population to the nearest in-network primary care providers to see if it would be prudent to contract with additional doctors in the area. You would need to collect at least three pieces of data:

• Addresses of your insured population (internal company source/database)
• Addresses of primary care provider offices (internal company source/database)
• Google Maps travel time API to calculate drive times between addresses (external data source)

In summary, data collection provides the fundamental pieces needed to solve your problem or answer your question.

1.3 Data Cleaning

We’ll discuss data cleaning in a little more detail in later chapters, but this phase generally refers to the process of taking the data you collected in step 1, and turning it into a usable format for your analysis. This phase can often be the most time consuming as it may involve handling missing data as well as pre-processing the data to be as error free as possible.

Depending on where you source your data will have major implications for how long this phase takes. For example, many of us actuaries benefit from devoted data engineers and resources within our companies who exert much effort to make our data as clean as possible for us to use. However, if you are sourcing your data from raw files on the internet, you may find this phase to be exceptionally difficult and time intensive.

1.4 Exploratory Data Analysis

Exploratory Data Analysis , or EDA, is an entire subject itself. In short, EDA is an iterative process whereby you:

• Generate questions about your data
• Search for answers, patterns, and characteristics of your data by transforming, visualizing, and summarizing your data
• Use learnings from step 2 to generate new questions and insights about your data

We’ll cover some basics of EDA in Chapter 4 on Data Manipulation and Chapter 5 on Data Visualization, but we’ll only be able to scratch the surface of this topic.

A successful EDA approach will allow you to better understand your data and the relationships between variables within your data. Sometimes, you may be able to answer your question or solve your problem after the EDA step alone. Other times, you may apply what you learned in the EDA step to help build a model for your data.

1.5 Model Building

In this step, we build a model, often using machine learning algorithms, in an effort to make sense of our data and gain insights that can be used for decision making or communicating to an audience. Examples of models could include regression approaches, classification algorithms, tree-based models, time-series applications, neural networks, and many, many more. Later in this module, we will practice building our own models using introductory machine learning algorithms.

It’s important to note that while model building gets a lot of attention (because it’s fun to learn and apply new types of models), it typically encompasses a relatively small portion of your overall analysis from a time perspective.

It’s also important to note that building a model doesn’t have to mean applying machine learning algorithms. In fact, in actuarial science, you may find more often than not that the actuarial models you create are Microsoft Excel-based models that blend together historical data, assumptions about the business, and other factors that allow you make projections or understand the business better.

1.6 Inference and Communication

The final phase of the framework is to use everything you’ve learned about your data up to this point to draw inferences and conclusions about the data, and to communicate those out to an audience. Your audience may be your boss, a client, or perhaps a group of actuaries at an SOA conference.

In any instance, it is critical for you to be able to condense what you’ve learned into clear and concise insights and convince your audience why your insights are important. In some cases, these insights will lend themselves to actionable next steps, or perhaps recommendations for a client. In other cases, the results will simply help you to better understand the world, or your business, and to make more informed decisions going forward.

1.7 Wrap-Up

As we conclude this chapter, take a few minutes to look at a couple alternative visualizations that others have used to describe the processes and components of performing analyses. What do they have in common?

• Karl Rohe - Professor of Statistics at the University of Wisconsin-Madison
• Chanin Nantasenamat - Associate Professor of Bioinformatics and Youtuber at the “Data Professor” channel

• School Guide
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• NCERT Soln. Class 8 Maths
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Chapter 1: Rational Numbers

• Rational Numbers
• Natural Numbers | Definition, Examples, Properties
• Whole Numbers - Definition, Properties and Examples
• Integers - Definition, Properties and Worksheet
• Rational Number: Definition, Examples, Worksheet
• Representation of Rational Numbers on the Number Line | Class 8 Maths
• Rational Numbers Between Two Rational Numbers | Class 8 Maths

Chapter 2: Linear Equations in One Variable

• Algebraic Expressions in Math: Definition, Example and Equation
• Linear Equations in One Variable
• Linear Equations in One Variable - Solving Equations which have Linear Expressions on one Side and Numbers on the other Side | Class 8 Maths
• Solve Linear Equations with Variable on both Sides
• Reducing Equations to Simpler Form | Class 8 Maths
• Equations Reducible to Linear Form

Chapter 3: Understanding Quadrilaterals

• Types of Polygons
• Triangles in Geometry
• Quadrilateral - Definition, Properties, Types, Formulas, Examples
• Area of Pentagon
• Sum of Angles in a Polygon
• Understanding Quadrilaterals - Measures of the Exterior Angles of a Polygon
• Kite - Quadrilaterals
• Introduction to Parallelogram: Properties, Types, and Theorem
• Properties of Parallelograms
• Rhombus: Definition, Properties, Formula, Examples

Chapter 4: Practical Geometry

• Construction of a Quadrilateral

Chapter 5: Data Handling

Data handling.

• What is Data Organization?
• Frequency Distribution
• Chance and Probability
• Random Experiment - Probability
• Probability in Maths

Chapter 6: Squares and Square Roots

• Squares and Square Roots
• How to Find Square Root of a Number?
• Pythagorean Triples

Chapter 7: Cubes and Cube Roots

• Cubes and Cube Roots
• Perfect Cube

Chapter 8: Comparing Quantities

• Ratios and Percentages
• Fractions - Definition, Properties, Types, Operations & Examples
• Discount Formula
• Sales Tax, Value Added Tax, and Goods and Services Tax - Comparing Quantities | Class 8 Maths
• Simple Interest
• Compound Interest | Class 8 Maths
• Compound Interest Formula

Chapter 9: Algebraic Expressions and Identities

• Algebraic Expressions and Identities | Class 8 Maths
• Types of Polynomials
• Like and Unlike Terms
• Mathematical Operations on Algebraic Expressions - Algebraic Expressions and Identities | Class 8 Maths
• Multiplying Polynomials
• Standard Algebraic Identities | Class 8 Maths

Chapter 10: Visualising Solid Shapes

• Visualizing Solid Shapes
• Mapping Space Around Us - Visualizing Solid Shapes | Class 8 Maths
• Faces, Edges And Vertices of 3D Shapes

Chapter 11: Mensuration

• Mensuration in Maths | Formulas for 2D and 3D Shapes, Examples
• Area of Trapezium - Mensuration | Class 8 Maths
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• Area of Rhombus: Formula, Derivation and Examples
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• Volume and Capacity - Mensuration | Class 8 Maths

Chapter 12: Exponents and Powers

• Laws of Exponents & Use of Exponents to Express Small Numbers in Standard Form - Exponents and Powers | Class 8 Maths
• Negative Exponents

Chapter 14: Factorisation

• Factorization of Polynomial
• Division of Algebraic Expressions

Chapter 15: Introduction to Graphs

• Introduction to Graphs | Class 8 Maths
• What is Linear Graph? Definition, Equation, Examples
• Cartesian Plane

Chapter 16: Playing with Numbers

• Playing with Numbers
• Letters for Digits
• Divisibility Rules
• CBSE Class 8 Maths Formulas
• NCERT Solutions for Class 8 Maths: Chapter Wise Solution PDF
• RD Sharma Class 8 Solutions for Maths: Chapter Wise PDF

Data Handling: Nowadays, managing and representing data systematically has become very important especially when the data provided is large and complex, This is when Data Handling comes into the picture.

Data handling involves the proper management of research data throughout and beyond the lifespan of a research project. This process includes establishing and implementing policies and procedures for securely storing, archiving, or disposing of data. It encompasses both electronic and non-electronic means of data management to ensure the data’s safety and security at all stages.

Statistics is another term for data handling, and it is useful not only in the fields of Math and Science but also in the fields where the representation of data is required. Let’s learn about some forms of Data handling, including the graphical representation of data and how they work.

Table of Content

What is Data Handling?

Graphical representation of data, pictographs, double- bar graph, line graphs, scatter plot, examples on data handling, data handling worksheet.

The definition of Data handling is in the title itself, that is, Handling the data in such a way that it becomes easier for people to understand and comprehend the given information. Hence, The process of collecting, Recording , and representing data in some form of graph or chart to make it easy for people to understand is called Data handling.

Data Handling Meaning

Data handling refers to the process of gathering, recording, organizing, and analyzing data to extract useful information, draw conclusions, and support decision-making. It encompasses a broad range of activities, including the collection of raw data, ensuring its accuracy and integrity, processing it into a manageable form, analyzing it statistically, and presenting it in ways that are easy to understand (such as charts, graphs, and tables).

• Pictographs or Picture Graphs

A pictograph is the pictorial representation of any data given to us in written form. It can be said that pictographs used to be the earliest form of conversation, since way back in time, people communicated mostly through pictures with each other since languages were not present.

Indeed, Pictograph plays a role in our day-to-day life too. For instance, when a friend tells us a story, we start imagining the story in our head and that makes it both easy to understand and easy to remember for a long time for us.

Drawing a Pictograph

Let’s learn to draw the pictograph with the help of an example,

Example: In a reading competition, three students were participating- Rahul, Saumya, and Ankush. They were supposed to read as many books as they could in an hour. Rahul read 3 books, Saumya read 2 books and Ankush read 4 books. Draw the pictograph for the information.

There are some basic steps to draw a Pictograph: Decide the particular picture/pictures that is required to represent data, make sure that the picture is a little related in order to memorize information easily. Here, to successfully read a book, a smiley is denoted. Now, draw the pictures according to information presented, for example, there will be 3 smilies for Rahul as he completed 3 books in an hour.

The graphical representation of any quantity, number or data in the form of bars is called a bar graph . With the help of Bar Graph, not only the data look neat and understanding but also it is easier to compare the data given.

Types of Bar Graph

Various types of bar graph include:

Vertical Bar Graph

Horizontal bar graph.

These are the most common bar graph we come across, the bars of grouped data in vertical bar graphs lie vertically. Sometimes when the data categorized have long names, then Horizontal bar graphs are preferred since, in vertical bar graphs, there is not much space on the x-axis.

An example explaining the concept of Bar graph is added below:

Example: There are 800 students in a school and the table for their birthdays in all 12 months is given below, Draw the Vertical Bar graph and answer,

• Maximum number of students have their birthdays in which month?
• Which two months have equal number of birthday?
• Minimum number of birthdays occur in which month?

The vertical bar graph for the table given in the question will be, From the Bar graph we can figure out the answer of the questions August is that month in which maximum birthdays are happening, since the bar above august is the longest(there are 110 students whose birthday come in August) From the graph, we can tell that January and April have equal lengths of bars, That means they have the same number of birthdays (both have 50 birthdays) Minimum number of birthdays occur in December since it has the smallest bar.(20 students have their birthdays in December.

The graphs that have their rectangular bars lying horizontally, which means that the frequency of the data lie on the x-axis while the categories of the data lie on the y-axis are known as Horizontal bar graphs.

Horizontal bar graphs are preferred when the name of the categories of data are long and the minimum space on the x-axis is not sufficient.

Example: In an examination, Reeta performed in 5 subjects, her performance is given in the table below. Draw a Horizontal Bar graph showing the marks she obtained in all the subjects, Also, calculate the overall Percentage obtained by her.

The Horizontal bar graph for the table mentioned in the question, The overall Percentage obtained by Reeta =  ×100 = 79 percent.

Double-bar graphs are used when two groups of data are required to be represented on a single graph. In a double-bar graph, to represent two groups of data, they are represented beside each other at different heights depending upon their values.

Advantages of double-bar graph:

• A double-bar graph is helpful when multiple data are required to be represented.
• It helps in summarizing large and big data in an easy and visual form.
• It shows and covers all different frequency distribution.

Example: The table for the number of boys and girls for classes 6, 7, 8, 9, and 10 is shown below. Represent the data on a Double-bar graph.

The double-bar graph for the table given the question,

Line graph or line chart visually shows how different things relate over time by connecting dots with straight lines. It helps us see patterns or trends in the data, making it easier to understand how variables change or interact with each other as time goes by.

How to Make a Line Graph?

To make a line graph we need to use the following steps:

• Determine Variables:  The first and foremost step to creating a line graph is to identify the variables you want to plot on the X-axis and Y-axis.
• Choose Appropriate Scales:  Based on your data, determine the appropriate scale.
• Plot Points:  Plot the individual data points on the graph according to the given data.
• Connect Points:  After plotting the points, you have to connect those points with a line.
• Label Axes:  Add labels to the X-axis and Y-axis. You can also include the unit of measurement.
• Add Title:  After completing the graph you should provide a suitable title.

Example: Kabir eats eggs each day and the data for the same is added in the table below. Draw a line graph for the given data

Pie chart is one of the types of charts in which data is represented in a circular shape. In pie chart circle is further divided into multiple sectors/slices; those sectors show the different parts of the data from the whole.

Pie charts, also known as circle graphs or pie diagrams, are very useful in representing and interpreting data

Example: In an office no of employees who plays various sports are added in a table below:

Draw suitable pie chart.

Required pie chart for the given data is,

A scatter plot is a type of graphical representation that displays individual data points on a two-dimensional coordinate system. Each point on the plot represents the values of two variables, allowing us to observe any patterns, trends, or relationships between them. Typically, one variable is plotted on the horizontal axis (x-axis), and the other variable is plotted on the vertical axis (y-axis).

Scatter plots are commonly used in data analysis to visually explore the relationship between variables and to identify any correlations or outliers present in the data.

Line drawn in a scatter plot, that is near to almost all the points in the plot is called the “line of best fit” or “trend line“. The example for the same is added in the image below:

People Also View:

Real Life Applications of Data Handling Difference Between Mean, Median, and Mode with Examples Chance and Probability

Example 1: In a survey done for a week from Monday to Sunday, for two cities Agra and Delhi, The temperatures of both the cities are measured and the temperatures obtained are as following,

Draw the Bar Graph for the given table in the question.

The Given table has two categories of data, one is the temperature for Agra and the other is the temperature for Delhi, Therefore, the graph can be drawn in one as a double- Bar graph, the graph shall look like,

Example 2: In a Theater, there are 3 Plays with different amounts of actors participating in each play. In play 1, there are 9 actors, in play 2, there are 3 lesser actors, and the number of actors in play 3 is one lesser than play 1. Draw the Pictograph for the information given and analyze in which play, the stage will be most crowded.

From the information given in the question, we can say that play 1 has 9 actors, play 2 has 6 actors and play 3 has 10 actors  Representing actors in the pictorial form as, Therefore, we can conclude that Play 3 has the stage most crowded as it has 10 actors acting on stage.

Example 3: In a Weather Report conducted for 5 weeks continuously, it was noted that not all days are sunny days in the season of spring. The observation said that week 1 had 4 sunny days, week 2 had 5 sunny days, week 3 had only 2 sunny days, week 4 had sunny days in the entire week, and week 5 had only 3 sunny days.

Draw a Pictograph for the information given above.

Representing sunny days in pictorial form for better understanding,

Question: The following are the scores of 7 students in mathematics: 10, 15, 20, 25, 10, 30, 20. Calculate the mean (average) score.

Question: Draw a bar graph for the following data showing the number of cars sold by a dealership in the first five months of the year: January (10), February (15), March (20), April (25), May (20).

Question: Find the median number of pets owned by the students in a class from the given list: 1, 3, 2, 2, 4, 3, 1, 2.

Question: Determine the mode of the following data set representing the number of books read by classmates in one month: 3, 4, 4, 2, 1, 5, 4, 3, 3.

Question: A pie chart shows the following distribution of a class’s favorite fruits: Apple (30%), Banana (25%), Cherry (20%), Date (25%). If there are 20 students in the class, how many students chose Banana as their favorite fruit?

Question: Calculate the range of the data given below, which shows the heights (in cm) of plants in a garden: 100, 150, 145, 133, 122, 139, 140, 150.

FAQs on Data Handling

Data Handling is the process of gathering, recording, and presenting information in a way that is helpful to analyze, make predictions and choices.

What are Two Types of Data Handling?

The two types of data handling are Qualitative Data Quantitative Data

What is data handling tools?

Some common data handling tools include: Database Management Systems (DBMS) Data Warehousing Tools Business Intelligence (BI) Tools Statistical Analysis Software, etc.

What are 4 types of data management?

Four types of data management include Relational Database Management Systems (RDBMS) Object-Oriented Database Management Systems (OODMBS) In-Memory Databases Columnar Databases

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5 Reasons Why Data Analytics is Important in Problem Solving

Data analytics  is important in problem solving and it is a key sub-branch of data science. Even though there are endless data analytics applications in a business, one of the most crucial roles it plays is problem-solving. 

Using data analytics not only boosts your problem-solving skills, but it also makes them a whole lot faster and efficient, automating a majority of the long and repetitive processes.

Whether you’re fresh out of university graduate or a professional who works for an organization, having top-notch  problem-solving skills  is a necessity and always comes in handy. 

Everybody keeps facing new kinds of complex problems every day, and a lot of time is invested in overcoming these obstacles. Moreover, much valuable time is lost while trying to find solutions to unexpected problems, and your plans also get disrupted often.

This is where data analytics comes in. It lets you find and analyze the relevant data without too much of human-support. It’s a real time-saver and has become a necessity in problem-solving nowadays. So if you don’t already use data analytics in solving these problems, you’re probably missing out on a lot!

As the saying goes from the chief analytics officer of TIBCO, 

“Think analytically, rigorously, and systematically about a  business problem  and come up with a  solution that leverages the available data .”  

– Michael O’Connell.

In this article, I will explain the importance of data analytics in problem-solving and go through the top 5 reasons why it cannot be ignored. So, let’s dive into it right away.

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What is Data Analytics?

Data analytics is the art of automating processes using algorithms to collect raw data from multiple sources and transform it. This results in achieving the data that’s ready to be studied and used for analytical purposes, such as finding the trends, patterns, and so forth.

Why is Data Analytics Important in Problem Solving?

Problem-solving and data analytics often proceed hand in hand. When a particular problem is faced, everybody’s first instinct is to look for supporting data. Data analytics plays a pivotal role in finding this data and analyzing it to be used for tackling that specific problem.

Although the analytical part sometimes adds further complexities, since it’s a whole different process that might get  challenging  sometimes, it eventually helps you get a better hold of the situation. 

Also, you come up with a more informed solution, not leaving anything out of the equation.

Having strong analytical skills help you dig deeper into the problem and get all the insights you need. Once you have extracted enough relevant knowledge, you can proceed with solving the problem. 

However, you need to make sure you’re using the  right, and complete  data, or using data analytics may even backfire for you. Misleading data can make you believe things that don’t exist, and that’s bound to take you off the track, making the problem appear more complex or simpler than it is.

Let’s see a very straightforward daily life example to examine the importance of data analytics in problem-solving; what would you do if a question appears on your exam, but it doesn’t have enough data provided for you to solve the question? 

Obviously, you won’t be able to solve that problem. You need a certain level of facts and figures about the situation first, or you’ll be wandering in the dark.

However, once you get the information you need, you can analyze the situation and quickly develop a solution. Moreover, getting more and more knowledge of the situation will further ease your ability to solve the given problem. This is precisely how data analytics assists you. It eases the process of collecting information and processing it to solve real-life problems.

5 Reasons Why Data Analytics Is Important in Problem Solving

Now that we’ve established a general idea of how strongly connected analytical skills and problem-solving are, let’s dig deeper into the top 5 reasons  why data analytics is important in problem-solving .

1. Uncover Hidden Details

Data analytics is great at putting the minor details out in the spotlight. Sometimes, even the most qualified data scientists might not be able to spot tiny details existing in the data used to solve a certain problem. However, computers don’t miss. This enhances your ability to solve problems, and you might be able to come up with solutions a lot quicker.

Data analytics tools have a wide variety of features that let you study the given data very thoroughly and catch any hidden or recurring trends using built-in features without needing any effort. These tools are entirely automated and require very little programming support to work. They’re great at excavating the depths of data, going back way into the past.

2. Automated Models

Automation is the future. Businesses don’t have enough time nor the budget to let manual workforces go through tons of data to solve business problems. 

Instead, what they do is hire a data analyst who automates problem-solving processes, and once that’s done, problem-solving becomes completely independent of any human intervention.

The tools can collect, combine, clean, and transform the relevant data all by themselves and finally using it to predict the solutions. Pretty impressive, right? 

However, there might be some complex problems appearing now and then, which cannot be handled by algorithms since they’re completely new and nothing similar has come up before. But a lot of the work is still done using the algorithms, and it’s only once in a blue moon that they face something that rare.

However, there’s one thing to note here; the process of automation by designing complex analytical and  ML algorithms  might initially be a bit challenging. Many factors need to be kept in mind, and a lot of different scenarios may occur. But once it goes up and running, you’ll be saving a significant amount of manpower as well as resources.

3. Explore Similar Problems

If you’re using a data analytics approach for solving your problems, you will have a lot of data available at your disposal. Most of the data would indirectly help you in the form of similar problems, and you only have to figure out how these problems are related. 

Once you’re there, the process gets a lot smoother because you get references to how such problems were tackled in the past.

Such data is available all over the internet and is automatically extracted by the data analytics tools according to the current problems. People run into difficulties all over the world, and there’s no harm if you follow the guidelines of someone who has gone through a similar situation before.

Even though exploring similar problems is also possible without the help of data analytics, we’re generating a lot of data  nowadays , and searching through tons of this data isn’t as easy as you might think. So, using analytical tools is the smart choice since they’re quite fast and will save a lot of your time.

4. Predict Future Problems

While we have already gone through the fact that data analytics tools let you analyze the data available from the past and use it to predict the solutions to the problems you’re facing in the present, it also goes the other way around.

Whenever you use data analytics to solve any present problem, the tools you’re using store the data related to the problem and saves it in the form of variables forever. This way, similar problems faced in the future don’t need to be analyzed again. Instead, you can reuse the previous solutions you have, or the algorithms can predict the solutions for you even if the problems have evolved a bit.

This way, you’re not wasting any time on the problems that are recurring in nature. You jump directly onto the solution whenever you face a situation, and this makes the job quite simple.

5. Faster Data Extraction

However, with the latest tools, the  data extraction  is greatly reduced, and everything is done automatically with no human intervention whatsoever. 

Moreover, once the appropriate data is mined and cleaned, there are not many hurdles that remain, and the rest of the processes are done without a lot of delays.

When businesses come across a problem, around  70%-80%  is their time is consumed while gathering the relevant data and transforming it into usable forms. So, you can estimate how quick the process could get if the data analytics tools automate all this process.

Even though many of the tools are open-source, if you’re a bigger organization that can spend a bit on paid tools, problem-solving could get even better. The paid  tools  are literal workhorses, and in addition to generating the data, they could also develop the models to your solutions, unless it’s a very complex one, without needing any support of data analysts.

What problems can data analytics solve? 3 Real-World Examples

Employee performance problems .

Imagine a Call Center with over 100 agents

By Analyzing data sets of employee attendance, productivity, and issues that tend to delay in resolution. Through that, preparing refresher training plans, and mentorship plans according to key weak areas identified.

Sales Efficiency Problems 

Imagine a Business that is spread out across multiple cities or regions

By analyzing the number of sales per area, the size of the sales reps’ team, the overall income and disposable income of potential customers, you can come up with interesting insights as to why some areas sell more or less than the others. Through that, prepping a recruitment and training plan or area expansion in order to boost sales could be a good move.

Business Investment Decisions Problems

Imagine an Investor with a portfolio of apps/software)

By analyzing the number of subscribers, sales, the trends in usage, the demographics, you can decide which peace of software has a better Return on Investment over the long term.

Throughout the article, we’ve seen various reasons why data analytics is very important for problem-solving. 

Many different problems that may seem very complex in the start are made seamless using data analytics, and there are hundreds of analytical tools that can help us solve problems in our everyday lives.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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Data Handling

We all come across such a huge amount of data whether the marks, attendance, numbers and a lot more.

We all need to arrange these data according to our need so that in future the accessibility of the data is easy. Here we are going to learn about the handling of the data and how it has to be done.

• A data is a collection of numbers gathered to give some information .
• Data is recorded according to the requirement and then it is stored either in a tabular form or some graphical or pictorial so that in future it can be accessed and used.
• The most common and easiest way to represent and store a data is through tally marks.
• Gathering and recording information and then presenting it in a way that it is meaningful to others is called as Data Handling.  

For example, when we watch a cricket match the runs scored by the batsmen, by the team, the wickets were taken by the bowler, the run rate etc are all recorded. These all are data and how the recording of data and presenting them in front of the viewers such that they are able to understand it is called as Data Handling.

Tally Chart

• The most commonly used method of data handling is the tally graph method.
• In this method, the data is represented using the straight horizontal sticks in a group of or less, where each stick represents a data.

We all can see in the following tally chart that which is the favorite part of Christmas of how many people and here each of the tally marks shows a single person who likes that part of Christmas.

• A pictograph represents data through pictures of objects. It helps answer the questions on the data at a glance.
• Pictographs are often used by dailies and magazines to attract reader’s attention.
• In simple words, in the pictograph, we use pictures to represent the data instead of tally marks.

Example 1 : The following pictograph represents the data of the number of books sold in a week at a library. After analyzing the graph answer the following questions.  

• Find the number of books sold on Saturday.
• Find the day on which the minimum and a maximum number of books are sold.

Solution : a) The number of books sold on Saturday was 7.

• b) The minimum number of books sold was on Thursday and Friday and maximum number was sold on Wednesday.  
• Since representing data in the form of pictographs is really difficult and consuming hence we require a better way to represent the data in a more effective manner.
• When bars of uniform width can be drawn horizontally or vertically with equal spacing between in such a manner that the length of each bar represents the given quantity.
• Such method of representing data is called a bar diagram or a bar graph.

Example 2: The following graphs shows the numbers of students whose birthdays fall on the month of January to February. Study the graph properly and answer the following question:

• Find the number of students who have their birthday in the month of May and August.
• Find the month on which the maximum number of students have their birthdays.  
• Find the months on which the same number of students have their birthdays.

Solution : After analyzing the graph

a) 8 students have their birthdays on May and only 1 student has his birthday in August.

b) On the month of June 10 students have their birthdays.

  c) On the month February and November 4 students have their birthdays.

Practice Questions

Q1) Analyze the graph and answer the following questions:  

How many apples are sold in the month of February?

b) In which month maximum and in which the minimum number of apples were sold?

Q2) Analyze the graph and answer the following questions:

  a) In which month maximum rainfall took place?

b) In which month minimum rainfall took place?

Recap  

• A data is a collection of numbers gathered to give some information.
• The graphical representation of data in vertical as well as horizontal bars is known as a bar graph.

Quiz for Data Handling

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Data handling

Unit of work

This series of five lessons is aimed at students aged 9-10. Students learn about data through a variety of unplugged activities. They write and evaluate algorithms and programs using selection and repetition to use the micro:bit as a temperature recorder, an automatic warning system and a digital assistant. You will ideally use physical micro:bits for these lessons, although you can also use the simulator.

Computational thinking :

Mathematics :

Information handling

Programming :

Download unit of work

Curriculum links

Overall key learning.

• can understand and apply the fundamental principles and concepts of computer science, including abstraction, logic, algorithms and data representation
• can analyse problems in computational terms, and have repeated practical experience of writing computer programs in order to solve such problems
• are responsible, competent, confident and creative users of information and communication technology

Additional skills

Researching , design thinking , problem-solving , debugging .

Lesson 1 : What is data?

Pupils learn about data by researching data about a person of their choosing and exploring ways data can be grouped. They consider the data that might be held about them and look at examples of data misuse by organisations.

Key learning:

• To understand what data is
• To classify data
• To identify ways that data might be used

Lesson 2 : Data treasure hunt

Pupils go on a treasure hunt around school to find data before learning about sensors and writing programs to record the temperature in different locations. They consider what the data they have collected shows and identify patterns.

• To understand that some devices uses sensors
• To write simple programs using sensors
• To use the BBC micro:bit to collect data

Lesson 3 : Sensor gadget design

Students develop their understanding of sensors through unplugged activities and by writing algorithms using repetition and selection. They then apply their understanding to design and evaluate a gadget using a sensor.

• To explain how repetition is used when programming sensors
• To follow design criteria to design a product
• To write algorithms that show how sensors will be used

Lesson 4 : Data conditions & selection

Pupils explore using data collected by the sensors on the micro:bit as a condition in programs. Then plan, program and test using the micro:bit as a temperature warning system.

• To know that data can be used as a condition in selection
• To explore the effects of changing the value of data in programs
• To write programs that use data as a condition

Lesson 5 : Digital assistants

Students explore using conditions in selection through unplugged activities before writing a program to enable the BBC micro:bit to be used as a digital assistant.

• To read and write algorithms using selection
• To identify how digital assistant might work
• To write a program to use a micro:bit as a digital assistant

This content is published under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) licence.

These lessons are mapped to the following learning objectives and standards for computing, geography, technologies, numeracy and mathematics:

England National Curriculum

Ks2 computing curriculum.

Curriculum aims:

Students should be taught to:

• design, write and debug programs that accomplish specific goals, including controlling or simulating physical systems; solve problems by decomposing them into smaller parts
• use logical reasoning to explain how some simple algorithms work and to detect and correct errors in algorithms and programs

Read the full KS2 computing curriculum .

KS2 geography curriculum

• use fieldwork to observe, measure, record and present the human and physical features in the local area using a range of methods including digital technologies

Read the full KS2 geography curriculum .

Years 3 & 4 science curriculum

Working scientifically:

• making systematic and careful observations and, where appropriate, taking accurate measurements using standard units, using a range of equipment, including thermometers and data loggers
• using results to draw simple conclusions, make predictions for new values, suggest improvements and raise further questions

Read the full KS2 science curriculum

KS2 DT curriculum

• technical knowledge - apply their understanding of computing to program, monitor and control their products

Read the full KS2 DT curriculum

Scotland Curriculum for Excellence

Technologies.

• I can extend and enhance my knowledge of digital technologies to collect, analyse ideas, relevant information and organise these in an appropriate way (TCH 2-01a)
• I can investigate how product design and development have been influenced by changing lifestyles (TCH 2-05a)
• I understand the operation of a process and its outcome. I can structure related items of information (TCH 2-13a)
• I can explain core programming language concepts in appropriate technical language (TCH 2-14a)
• I can create, develop, and evaluate computing solutions in response to a design challenge (TCH 2-15a)

Read the full Curriculum for Excellence: technologies .

Numeracy and mathematics

• I have carried out investigations and surveys, devising and using a variety of methods to gather information and have worked with others to collate, organise and communicate the results in an appropriate way (MNU 2-20b)
• I can display data in a clear way using a suitable scale, by choosing appropriately from an extended range of tables, charts, diagrams and graphs, making effective use of technology (MTH 2-21a)

Read the full Curriculum for Excellence: numeracy and mathematics .

Northern Ireland Curriculum - Primary

Using ict across the curriculum.

Pupils should be taught to:

• explore - access and manage data and information
• explore - research, select, process and interpret information
• explore - investigate, make predictions and solve problems through interaction with digital tools
• express - create information and multimedia products using a range of assets

KS2 - suggested curriculum ideas for the world around us

• design and make models

Read the full Northern Ireland Curriculum - Primary

KS1 & 2 - requirements for using ICT

Read the full KS1 & 2 requirements for using ICT

Primary using ICT - desirable features - computational thinking and coding

Pupils should:

• create a more sophisticated coding project using a broad range of commands; and/or
• solve a given problem using commands in a programming environment.

Programmable devices (such as Parrot Drone, micro:bit or Sphere)

• look at and talk about examples of coding projects, including the use of motion, looks, lights or sounds, sensors, control and events such as ‘if...then’ and ‘loop until’ (or equivalent) that make the code more efficient;
• recognise that these projects are composed of different components and break the task into smaller manageable tasks (decomposition);
• in small groups, plan and storyboard their own coding project, working out what different parts of the program must do, using logical reasoning to discuss and compare the commands that are required for their algorithm;
• use a range of commands to create a project including triggering commands such as ‘if...then’ and ‘loop until’ to facilitate a more efficient method of interaction;
• test and debug at regular intervals and collaborate with others to solve problems as they arise;
• share their work (possibly using digital tools), respond to feedback and comment on others’ work; and
• organise files and export work in an appropriate format so that others may view it.

Read all Primary using ICT desirable features

Curriculum for Wales

Science and technology.

Progression step 2 - computation is the foundation for our digital world:

• I can safely use a range of tools, materials and equipment to construct for a variety of reasons
• I can use computational thinking techniques, through unplugged or offline activities
• I can create simple algorithms and am beginning to explain errors
• I can follow instructions to build and control a physical device

Progression step 3 - computation is the foundation for our digital world:

• I can use conditional statements to add control and decision-making to algorithms
• I can identify repeating patterns and use loops to make my algorithms more concise
• I can explain and debug algorithms
• I can use sensors and actuators in systems that gather and process data about the systems’ environment

Read the full science and technology curriculum

Digital competence framework

Progression step 1 - data and computational thinking - problem-solving and modelling:

• I can recognise and follow instructions in the appropriate order to perform a task.
• I can organise, select and use simple language to give instructions to others.
• I can control devices giving instructions.
• I can identify errors in simple sets of instructions (algorithm).

Progression step 2 - data and computational thinking - problem-solving and modelling:

• I can detect and correct mistakes which cause instructions (a solution) to fail (debug).
• I can create and record verbal, written and symbolic instructions to test ideas, e.g. the order of waking up through a diagram or flowchart.
• I can change instructions to achieve a different outcome.

Progression step 3 - data and computational thinking - problem-solving and modelling:

• I can understand the importance of the order of statements within algorithms.

Progression step 1 - data and computational thinking – data information literacy:

• I can collect data found in my environment.
• I can sort and classify objects using one criterion.
• I can present and evaluate my data by creating simple charts, e.g. pictogram.

Progression step 2 - data and computational thinking – data information literacy:

• I can collect, enter, organise and analyse data into different groups or formats, e.g. tables, charts, databases and spreadsheets.
• I can extract and evaluate information from tables and graphs to answer questions.

Progression step 1 - producing – creating digital content:

• I can create simple digital work.

Read the digital competence framework

USA Code.org

Cs fundamentals.

Courses C, D, E

Concepts included:

• algorithms & programs using nested loops & conditionals (if/else if)
• variables (strings)

Read the full Code.org CS Fundamentals curriculum .

USA CSTA Standards

• 1B-CS-01 - Describe how internal and external parts of computing devices function to form a system.
• 1B-CS-02 - Model how computer hardware and software work together as a system to accomplish tasks
• 1B-CS-03 - Determine potential solutions to solve simple hardware and software problems using common troubleshooting strategies.
• 1B-DA-06 - Organize and present collected data visually to highlight relationships and support a claim.
• 1B-DA-07 - Use data to highlight or propose cause-and-effect relationships, predict outcomes, or communicate an idea.
• 1B-AP-08 - Compare and refine multiple algorithms for the same task and determine which is the most appropriate
• 1B-AP-09 - Create programs that use variables to store and modify data.
• 1B-AP-10 - Create programs that include sequences, events, loops, and conditionals.
• 1B-AP-11 - Decompose (break down) problems into smaller, manageable subproblems to facilitate the program development process.
• 1B-AP-12 - Modify, remix, or incorporate portions of an existing program into one's own work, to develop something new or add more advanced features.
• 1B-AP-13 - Use an iterative process to plan the development of a program by including others' perspectives and considering user preferences.
• 1B-AP-14 - Debug (identify and fix) errors in an algorithm or program that includes sequences and simple loops.
• 1B-AP-15 - Test and debug (identify and fix errors) a program or algorithm to ensure it runs as intended.
• 1B-AP-17 - Describe choices made during program development using code comments, presentations, and demonstrations.

Read the CSTA Standards in full .

You may also be interested in

This unit of four lessons is suitable for students aged 7-8 years. They create nature representations, firstly using art materials and are introduced to computational thinking and programming the LEDs on the BBC micro:bit. 

Digital flashcards

Five lessons aimed at students aged 7-8 years. Students design and program digital flashcards to help them learn a foreign language using the BBC micro:bit.

Volcano animations

Five lessons aimed at students aged 8-9 years. Students explore animations, developing their computational thinking and use repetition to create volcanic eruption animations using the LEDs on the BBC micro:bit.

The resources on this page will hopefully help you teach AO2 and AO3 of the new GCSE specification - problem solving and reasoning.

This brief lesson is designed to lead students into thinking about how to solve mathematical problems. It features ideas of strategies to use, clear steps to follow and plenty of opportunities for discussion.

The PixiMaths problem solving booklets are aimed at "crossover" marks (questions that will be on both higher and foundation) so will be accessed by most students. The booklets are collated Edexcel exam questions; you may well recognise them from elsewhere. Each booklet has 70 marks worth of questions and will probably last two lessons, including time to go through answers with your students. There is one for each area of the new GCSE specification and they are designed to complement the PixiMaths year 11 SOL.

These problem solving starter packs are great to support students with problem solving skills. I've used them this year for two out of four lessons each week, then used Numeracy Ninjas as starters for the other two lessons.  When I first introduced the booklets, I encouraged my students to use scaffolds like those mentioned here , then gradually weaned them off the scaffolds. I give students some time to work independently, then time to discuss with their peers, then we go through it as a class. The levels correspond very roughly to the new GCSE grades.

Some of my favourite websites have plenty of other excellent resources to support you and your students in these assessment objectives.

@TessMaths has written some great stuff for BBC Bitesize.

There are some intersting though-provoking problems at Open Middle.

I'm sure you've seen it before, but if not, check it out now! Nrich is where it's at if your want to provide enrichment and problem solving in your lessons.

MathsBot  by @StudyMaths has everything, and if you scroll to the bottom of the homepage you'll find puzzles and problem solving too.

I may be a little biased because I love Edexcel, but these question packs are really useful.

The UKMT has a mentoring scheme that provides fantastic problem solving resources , all complete with answers.

I have only recently been shown Maths Problem Solving and it is awesome - there are links to problem solving resources for all areas of maths, as well as plenty of general problem solving too. Definitely worth exploring!