What you will learn

• Data information and knowledge
• Types of data
• Data analytics process
• Data analytics lifecycle
• Applications of data analytics
• Data collection
• Sampling techniques
• Data cleaning
• Data preprocessing
• Data transformation
• Measures of central tendency
• Measures of dispersion
• Correlation
• Probability basics
• Exploratory data analysis
• Data visualization
• Histograms
• Box plots
• Scatter plots
• Hypothesis testing
• Supervised and unsupervised learning
• Classification
• Regression
• Naive Bayes
• K-Nearest Neighbour
• Linear regression
• Clustering
• K-Means algorithm
• Hierarchical clustering
• Association rule mining
• Apriori algorithm
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Data import and export
• Data manipulation
• Big data fundamentals
• Hadoop ecosystem
• HDFS
• YARN
• MapReduce

Course content (46 lessons)

1. About This Course — Introduction to Data Analytics — University Course This course covers Introduction to Data Analytics as taught in undergraduate/postgraduate BCA/MCA/BTech university courses. The…
2. Unit 1 — Data, Information and Knowledge — Data, Information and Knowledge Everything in data analytics starts with a simple distinction that students routinely get wrong in exams: data is not information, and information…
3. Unit 1 — Types of Data — Types of Data Data is classified along several independent axes. Every axis matters, because the type of data decides which statistical measure and which chart is valid . Using a…
4. Unit 1 — The Data Analytics Process — The Data Analytics Process The data analytics process is the sequence of steps that turns a business question into a data-backed answer. Different books use slightly different…
5. Unit 1 — Data Analytics Lifecycle — Data Analytics Lifecycle The Data Analytics Lifecycle is the structured, six-phase framework (popularised by EMC/Dell in Data Science and Big Data Analytics ) that governs how a…
6. Unit 1 — Types of Data Analytics — Types of Data Analytics Analytics is conventionally divided into four types , ordered by increasing difficulty and increasing business value. The Four Types Type Question…
7. Unit 1 — Applications of Data Analytics — Applications of Data Analytics Data analytics is now embedded in essentially every sector. This lesson surveys the major domains with concrete, exam-quotable use cases. (Unit 4…
8. Unit 1 — Data Collection — Data Collection Data collection is the systematic gathering of data relevant to the defined problem. It is the second step of the analytics process, and every downstream result…
9. Unit 1 — Sampling Techniques — Sampling Techniques Sampling is the process of selecting a subset (sample) from a larger group ( population ) so that conclusions drawn from the sample can be generalised to the…
10. Unit 1 — Data Cleaning — Data Cleaning Data cleaning (data cleansing / scrubbing) is the process of detecting and correcting corrupt, inaccurate, incomplete, duplicate or irrelevant records in a dataset.…
11. Unit 1 — Data Preprocessing — Data Preprocessing Data preprocessing is the broader stage that converts raw data into a form suitable for analysis and modelling. Data cleaning is one component of it; the full…
12. Unit 1 — Data Transformation — Data Transformation Data transformation converts data from one format, scale or structure into another so that algorithms can use it effectively. It is the final preparation step…
13. Unit 2 — Measures of Central Tendency — Measures of Central Tendency A measure of central tendency is a single value that represents the centre or "typical" value of a dataset. The three classical measures are the mean…
14. Unit 2 — Measures of Dispersion — Measures of Dispersion Dispersion (variability/spread) measures how far the data values are scattered from the centre. Two datasets can share an identical mean yet be completely…
15. Unit 2 — Correlation and Covariance — Correlation Correlation measures the strength and direction of the linear relationship between two variables. It is the first bivariate technique an analyst reaches for.…
16. Unit 2 — Probability Basics — Probability Basics Probability quantifies uncertainty — how likely an event is to occur. It is the mathematical foundation of hypothesis testing (later in this unit), Naïve Bayes…
17. Unit 2 — Probability Distributions — Probability Distributions A probability distribution describes how probability is spread across the possible values of a random variable. Knowing the distribution lets you compute…
18. Unit 2 — Exploratory Data Analysis (EDA) — Exploratory Data Analysis (EDA) Exploratory Data Analysis , introduced by John Tukey in 1977, is the approach of analysing datasets to summarise their main characteristics —…
19. Unit 2 — Data Visualization Principles — Data Visualization Data visualization is the graphical representation of data and information. The human visual system detects patterns, trends and outliers in a picture far…
20. Unit 2 — Histograms — Histograms A histogram displays the frequency distribution of a single continuous numeric variable by dividing its range into consecutive, non-overlapping intervals ( bins ) and…
21. Unit 2 — Box Plots — Box Plots (Box-and-Whisker Plots) A box plot , invented by John Tukey, summarises a numeric distribution using the five-number summary and explicitly marks outliers. It is the…
22. Unit 2 — Scatter Plots — Scatter Plots A scatter plot displays the relationship between two numeric variables by plotting one point per observation at coordinates (x, y). It is the fundamental tool of…
23. Unit 2 — Introduction to Hypothesis Testing — Introduction to Hypothesis Testing Hypothesis testing is the formal statistical procedure for deciding whether sample evidence is strong enough to support a claim about a…
24. Unit 3 — Supervised and Unsupervised Learning — Supervised and Unsupervised Learning Machine Learning (ML) is the branch of AI in which systems learn patterns from data rather than being explicitly programmed with rules. In…
25. Unit 3 — Classification — Classification Classification is the supervised learning task of predicting a categorical class label for a new observation, using a model learned from labelled training data.…
26. Unit 3 — Regression — Regression Regression is the supervised learning task of predicting a continuous numeric value from one or more input variables. Where classification answers "which class?",…
27. Unit 3 — Naïve Bayes Classifier — Naïve Bayes Classifier Naïve Bayes is a probabilistic classifier based on Bayes' theorem with a strong ("naïve") assumption that all features are conditionally independent given…
28. Unit 3 — K-Nearest Neighbour (KNN) — K-Nearest Neighbour (KNN) KNN classifies a new observation by looking at the k closest training examples and taking a majority vote among their labels. It is the simplest possible…
29. Unit 3 — Linear Regression — Linear Regression Linear regression models the relationship between a dependent variable Y and one or more independent variables X by fitting a straight line (or hyperplane) that…
30. Unit 3 — Linear Regression: End-to-End Project — Linear Regression — A Complete Worked Project This lesson walks through a full regression project on a realistic dataset, applying every stage from Units 1–3: loading, EDA,…
31. Unit 3 — Clustering — Clustering Clustering is the unsupervised task of grouping a set of objects so that objects in the same group ( cluster ) are more similar to each other than to objects in other…
32. Unit 3 — K-Means Algorithm — K-Means Clustering Algorithm K-Means partitions n observations into k clusters , where each observation belongs to the cluster with the nearest mean (centroid) . It is the most…
33. Unit 3 — Hierarchical Clustering — Hierarchical Clustering Hierarchical clustering builds a tree (hierarchy) of nested clusters rather than a single flat partition. Its great advantage over K-Means: you do not have…
34. Unit 3 — Association Rule Mining — Association Rule Mining Association rule mining discovers interesting relationships, correlations, or frequent co-occurrence patterns among items in large transactional datasets.…
35. Unit 3 — Apriori Algorithm — The Apriori Algorithm Apriori (Agrawal and Srikant, 1994) is the classic algorithm for mining frequent itemsets and generating association rules. It solves the combinatorial…
36. Unit 4 — Introduction to NumPy — NumPy — Numerical Python NumPy is the foundational package for numerical computing in Python. Every other library in the analytics stack — Pandas, Matplotlib, scikit-learn, SciPy…
37. Unit 4 — Pandas: Series and DataFrame — Pandas — Python Data Analysis Library Pandas (from " pan el da ta") provides fast, flexible labelled data structures built on NumPy. It is the single most-used tool in a data…
38. Unit 4 — Data Import and Export — Data Import and Export Every analytics project starts by reading data in and ends by writing results out. Pandas provides a uniform read / to API across a dozen formats. Format…
39. Unit 4 — Data Manipulation with Pandas — Data Manipulation Data manipulation (also called data wrangling or munging) is the process of reshaping, combining, aggregating and deriving from a dataset until it answers the…
40. Unit 4 — Data Visualization with Matplotlib — Matplotlib Matplotlib is Python's foundational plotting library. Its pyplot module provides a MATLAB-like interface, and virtually every other Python visualization library…
41. Unit 4 — Statistical Visualization with Seaborn — Seaborn Seaborn is a statistical data visualization library built on Matplotlib. It provides beautiful defaults, works directly with Pandas DataFrames, and produces complex…
42. Unit 4 — Big Data Fundamentals — Big Data Fundamentals Big Data refers to datasets so large, fast-moving or varied that traditional data-processing tools (a single machine, a relational database, Pandas in…
43. Unit 4 — The Hadoop Ecosystem — Apache Hadoop Apache Hadoop is an open-source framework for the distributed storage and distributed processing of very large datasets across clusters of commodity hardware.…
44. Unit 4 — MapReduce — MapReduce MapReduce is a programming model and processing framework for generating and processing large datasets in parallel across a distributed cluster. The programmer writes…
45. Unit 4 — Applications of Data Analytics & Course Wrap-Up — Applications of Data Analytics — Bringing It All Together This final lesson revisits applications with everything you have learned across the four units, tracing complete…
46. Previous Year Questions — Introduction to Data Analytics — Previous Year Questions PYQ papers for this course will be added here soon. Check back later for: - End Term Examination papers - Mid Term papers…

About This Course

Introduction to Data Analytics — University Course

This course covers Introduction to Data Analytics as taught in undergraduate/postgraduate BCA/MCA/BTech university courses. The syllabus is divided into 4 units — data fundamentals and preparation, statistical foundations with exploratory data analysis, predictive and descriptive analytics (machine learning), and Python tooling with big data technologies.

Syllabus at a Glance:

What you will learn:

• How raw data becomes information, then knowledge, then a decision — and the full lifecycle a real analytics project follows
• How to collect, sample, clean, preprocess, and transform messy real-world data into an analysis-ready dataset
• The statistics an analyst actually uses daily — central tendency, dispersion, correlation, probability, and hypothesis testing
• Exploratory Data Analysis with histograms, box plots, and scatter plots, and how to read what each chart is telling you
• Core machine learning algorithms — Naïve Bayes, KNN, linear regression, K-Means, hierarchical clustering, and Apriori — with worked hand calculations and runnable Python
• The practical Python stack (NumPy, Pandas, Matplotlib, Seaborn) plus how big data systems (Hadoop, HDFS, MapReduce) scale analytics beyond one machine

PYQ papers are available at the end of the lesson list.

Continue reading: About This Course →

Frequently asked questions

Is the Introduction to Data Analytics course really free?

Yes. The entire Introduction to Data Analytics course on Siksha Sarovar is free to read with no account required. You can optionally sign in with Google to save your progress.

Do I get a certificate for Introduction to Data Analytics?

Yes — finish the lessons and pass the quiz to earn a free, verifiable certificate you can share on LinkedIn or with recruiters.

Can I run code while learning?

Yes. The built-in online compiler runs C, C++, Python, Java, PHP, JavaScript, C# and SQL directly in your browser — no installation needed.