CBSE Class 10 Data Science (419)
Chapters: 6
1. Part A: Employability Skills – II
Communication Skills-II · Self-Management Skills-II · ICT Skills-II · Entrepreneurial Skills-II · Green Skills-II
- Communication Skills: How to Share Meaning Clearly and Kindly – Communication is sharing meaning between people. A sender encodes an idea into a message, sends it through a channel, and a receiver decodes it and gives feedback. Barriers such as noise, hard words, strong emotions and cultural differences can block or bend the message. We send meaning with words (verbal), with face, eyes, gestures, posture and tone (non-verbal), and with pictures (visual). Active listening — attention, patience, questions and paraphrasing — proves the message arrived. Good communicators adapt to context: formal or informal, spoken or written, and use polite requests, kind refusals, sincere apologies and assertive (not aggressive or passive) language.
- Self-Management: Taking Charge of Your Feelings, Goals and Time – Self-management is the skill of guiding your own feelings, thoughts and actions so you can reach your goals. It starts with self-awareness: noticing what you feel and how strong it is. Then you pause before reacting (Stop, Think, Act) and use calming tools such as slow breathing. Your beliefs and thoughts shape your feelings and actions, so changing an unhelpful thought ('I'm useless') into a helpful one ('I can't do it yet') changes what you do. Good self-managers set SMART goals, plan their time by importance, build healthy habits and bounce back from setbacks (resilience). These skills help in school, friendships and future careers.
- ICT Basics: Using Computers, the Cloud and Learning Platforms Safely – ICT means information and communication technology: the devices, software and networks we use to create, store and share information. Every computer takes input, processes it, gives output and stores data. Storage is measured in bytes, and each bigger unit (KB, MB, GB, TB) is about 1000 times the one before. Good digital learners keep files in clearly named folders, use learning platforms to get and submit work, store files in the cloud and on devices, back up with the 3-2-1 rule and protect accounts with long passphrases and two-step login. ICT also lets you make creative projects such as videos, websites and podcasts.
2. Part B: Use of Statistics in Data Science
Use of Statistics in Data Science
- Statistics: Mean, Median and Mode of Grouped Data – When data is put into classes (like marks 20–30), we cannot see each value. We use the middle of each class to find the mean, the running total to find the median, and the tallest bar to find the mode. All three tell us the 'centre' of the data in different ways.
3. Part B: Distributions in Data Science
Distributions in Data Science
- Probability Distributions of Discrete Random Variables – A random variable X turns each outcome of an experiment into a number. Its probability distribution lists every value x with its probability P(X = x); each P is between 0 and 1 and they add to 1. The mean E(X) = Σx·P(x) is the long-run average (balance point). The variance Var(X) = Σ(x − μ)²P(x) = E(X²) − μ² measures spread; σ = √Var. Special models: uniform, binomial B(n, p) with mean np and variance np(1 − p), and Poisson with mean = variance = λ.
4. Part B: Identifying Patterns
Identifying Patterns
- Data Literacy: Reading, Using and Questioning Data – Data literacy means being able to read data, work with it and ask good questions about it. A dataset is a table: each row is a case (a person, a day, a shop) and each column is a variable (age, travel mode, price). Every dataset shows only a limited picture: it covers some cases, some variables, some time and was collected in a certain way, so it can be biased or out of date. Researching with a dataset follows steps: ask a question, find or collect data, clean it, sort, filter and count, make a chart, draw a careful conclusion. Open data is data anyone may use for free, usually anonymised first. Organisations collect data about us (purchases, location, clicks) to build profiles, target adverts and plan services, which brings benefits but also privacy risks and rules such as consent.
5. Part B: Data Merging
Data Merging
- Data Literacy: Reading, Using and Questioning Data – Data literacy means being able to read data, work with it and ask good questions about it. A dataset is a table: each row is a case (a person, a day, a shop) and each column is a variable (age, travel mode, price). Every dataset shows only a limited picture: it covers some cases, some variables, some time and was collected in a certain way, so it can be biased or out of date. Researching with a dataset follows steps: ask a question, find or collect data, clean it, sort, filter and count, make a chart, draw a careful conclusion. Open data is data anyone may use for free, usually anonymised first. Organisations collect data about us (purchases, location, clicks) to build profiles, target adverts and plan services, which brings benefits but also privacy risks and rules such as consent.
6. Part B: Ethics in Data Science
Ethics in Data Science
- AI Ethics: Using Artificial Intelligence Fairly and Safely – AI systems learn patterns from data and then make decisions. AI ethics asks whether those decisions are fair, safe and respectful. The main issues are bias (unfair data gives unfair results), privacy (personal data needs consent and protection), transparency (people should know why an AI decided something), accountability (a human stays responsible), safety and misuse (deepfakes, false information), and social impact (jobs, the digital divide, the environment). Responsible AI means checking all of these before and after an AI is used.