Data Analytics course syllabus includes topics and practical exercises that can teach students how to extract, analyze, and manipulate data to draw conclusions or insights. It also teaches about various Data Analytics tools and software that help in the analysis of data. Hence, the essential Data Analytics subjects include Probability and Statistics, Data Structures and Algorithms, Data Simulation, Data Collection, and similar.
As Data Analytics courses are available at different levels (diploma, certificate, undergraduate, and postgraduate), the combination of different data analytics subjects and the Data Analytics course curriculum may vary. The important subjects in Data Analytics included in almost every kind of Data Analytics program are types of Data Analytics, Statistical Analysis, Excel, SQL, Tableau, Power BI, etc. Expertise in Data Analytics Tools and languages such as Python, Machine Learning, Big Data, and SQL is beneficial, as these are the top listed skills for the Data Analytics job profiles as per the top job listing websites.
| Data Science Course Syllabus | Business Analytics Course Syllabus |
| Python Course Syllabus | Java Course Syllabus |
The Elements of Data Mining, Statistical Learning, Inference, and Prediction, Data Analysis Software: Programming with R (Statistics and Computing), and Probability & Statistics for Engineers & Scientists are the top data analytics books recommended by experts and professionals in the field of data analytics.
Table of Contents
1.1 Big Data Analytics Syllabus
1.2 BSc Computer Science with Data Analytics Syllabus
1.3 MBA in Data Analytics Syllabus
1.4 BSc Data Analytics Syllabus
2.1 Data Structure and Algorithms
2.2 Probability and Statistics
2.4 Text Analytics
2.5 Data Collection
3.1 Python
3.2 Microsoft Excel
3.3 R Programming
3.4 SQL
3.5 Machine Learning
Data Analytics Course Syllabus
The Data Analytics course syllabus may differ from course to course or curriculum to curriculum but there are a few common data analytics subjects that are mentioned below:
| Data Structures and Algorithms | Supply Chain Analytics |
| Probability and Statistics | Customer Analytics |
| Relational Database Management Systems | Retail Analytics |
| Business Fundamentals | Social Network Analysis |
| Text Analytics | Pricing Analytics |
| Data Collection | Marketing Analytics |
| Data Visualization | Optimization |
| Statistical Analysis | Machine Learning |
| Forecasting Analytics | Simulation |
Big Data Analytics Syllabus
Big Data Analytics is the process of extracting useful information from huge volumes of data. This includes finding hidden patterns, market trends, future predictions, and correlations. The following table mentions the MSc Big Data Analytics syllabus from St. Xavier’s University, Mumbai. A similar Big Data Analytics curriculum is usually followed in all of the colleges. Check: Top 10 Big Data Analytics Courses Online
| Semester I | Semester II |
|---|---|
| Statistical Methods | Foundations of Data Science |
| Probability & Stochastic Process | Advanced Statistical Methods |
| Linear Algebra & Linear Programming | Machine Learning I |
| Computing for Data Sciences using R, Python, and Java | Enabling Technologies for Data Science I (Theory & Lab) |
| Database Management – Relational and Non-Relational | Value Thinking |
| Python Programming (Theory & Lab) | Elective Course |
| Semester III | Semester IV |
| Enabling Technologies for Data Science 2 (Theory & Lab) | Internship based Project work |
| Machine Learning 2 including Deep Learning | - |
| Data Visualization with Tableau | - |
| Modeling in Operations Management | - |
| Elective Courses | - |
BSc Computer Science with Data Analytics Syllabus
The following table mentions the BSc Computer Science with Data Analytics syllabus followed by Bharathiar University, Tamil Nadu. The other colleges also follow the same curriculum. The electives offered might be different.
| Semester I | Semester II |
|---|---|
| Language - I | Language - II |
| English - I | English - II |
| Programming in C | Programming in C++ |
| Programming Lab - C | Internet Basics Lab |
| Data Structures | Discrete Mathematics |
| Introduction to Linear Algebra | Value Education – Human Rights |
| Environmental Studies | - |
| Semester III | Semester IV |
| Java Programming | Python Programming |
| Java Programming - Lab | Data Warehousing & Data Mining |
| Database Management Systems | Python Programming Lab |
| Data Communication & Networks | Deep Learning |
| Data Visualization | Capstone Project Work Phase I |
| Elective Course | Elective Course |
| Semester V | Semester VI |
| R Programming | Linux & Shell Programming |
| R Programming Lab | Linux & Shell Programming Lab |
| Big Data Analytics | Project Work Lab |
| Elective Course | Elective Course I |
| Capstone Project Work Phase II | Elective Course II |
| - | Elective Course III |
| - | Machine Learning |
| - | Extension Activities |
MBA in Data Analytics Syllabus
The following table mentions the MBA in Data Analytics syllabus followed by Sharda University, Greater Noida. The other colleges also follow the same curriculum. The electives offered might be different. Also Check: Top MBA in Data Analytics Colleges
| Semester I | Semester II |
|---|---|
| Accounting for Managers | Applied Operations Research |
| Applied Statistics for Decision Making | Data Cleaning, Normalization and Data Mining |
| Financial Analysis and Reporting | Econometrics |
| Macroeconomics in the Global Economy | Foundation course in Business Analytics |
| Organizational Behavior | Project Management |
| Research Methodology | Spreadsheet Modeling |
| Semester III | Semester IV |
| Applied Business Analytics | Ethical and Legal Aspects of Analytics |
| Foundation Course in Descriptive Analysis | Healthcare Analytics |
| Foundation Course on Predictive Analysis | HR Analytics |
| SAP FICO | Project Work |
| SAP HCM | R Programming |
| Stochastic Modeling | Social and Web Analytics |
BSc Data Analytics Syllabus
| Semester I | Semester II |
|---|---|
| Foundation Course in Mathematics | Linear Algebra |
| Discrete Mathematics | Statistics II |
| Statistics I | Statistics III |
| Environmental Science | Differential Equations & Complex Variable |
| Communicative English I | Introduction to Computer Organization |
| Fundamentals of Computer & Problem Solving using C | Data Structure & Algorithms |
| R Programming | Introduction to MATLAB in Data Analysis |
| Semester III | Semester IV |
| Numerical Analysis | Text Analytics |
| Data Preparation & Data Cleaning | Regression, Time Series, Forecasting and Index Numbers |
| Database Management Systems | Multivariate Analysis |
| Data Warehousing & Data Mining | Statistical Inference |
| Operating Systems | Recommender Systems |
| OOPS using Python | Data Visualization |
| Community Connect | - |
| Semester V | Semester VI |
| Statistical Analysis | Deep Learning |
| Data Scientist Toolbox | Big Data Analytics |
| Machine Learning | Elective - II |
| Statistical Simulation | Elective - III |
| Operational Research | Capstone Project |
| Elective - I | Research Report Writing & Presentation |
Data Analytics Subjects
The following are the details of some of the important subjects of the data analytics course syllabus.
Also Check:
| Top 10 Data Analytics Certification Courses | Top 10 Business Analytics Certification Courses | Top 30 Data Science Certification Courses |
Data Structure and Algorithms
| Array, Iteration, and Invariants | List, recursion, stacks, and queues |
| Efficiency and complexities | Trees |
| Hash Tables | Binary search trees |
| Searching | Sorting |
Probability and Statistics
| Probability models | Random Variable and distribution |
| Model Checking | Relationship among variable |
| Sampling distributions and Limits | Statistical inferences |
| Expectations | Optimal Inferences |
| Bayesian Inferences | - |
Business Fundamentals
| Teamwork in Business | The foundations of Business |
| Ethics and Social responsibility | Structuring organizations |
| Motivating Employees | Managing Human resources |
| Economics of Business | Operations Management |
Text Analytics
| Natural language basics | Processing and understanding text |
| Text Summarization | Text similarity and Clustering |
| Text classification | Semantic and Sentiment analysis |
Data Collection
| Survey Sampling | Observational result |
| Statistical Techniques | Analysis of Unstructured Data |
| Extracting and Presenting Statistics | - |
Data Visualization
| Java | CSS |
| Customized geographic map | Creation of Bar Chart, Scatter Plot |
Top Data Analytics Skills
According to the report of the World Economic Forum, most companies will be hiring Data Analysts from next year onwards. So on that note, candidates must be aware of the skills that will help them get a good position as Data Analysts.
And to be good at data analytics, they need to have strong numerical and analytical skills and must have a proper understanding of computer software like Scripting Language (Python), Querying Language (SQL), Statistical Language (R), Machine Learning, and Microsoft Excel.
Python
| File operations using Python | Looping in Python |
| Python Syntax | Functions, Function Arguments, and Control Flow |
| Working with Lists | Python Modules |
| Decorators and generators | Using Dictionaries |
| Errors and Exception Handling | Comparisons and Operators |
Check: Top 10 Python Certification Courses Online
Microsoft Excel
| Creating Workbooks | Formatting Data |
| Using Formulas | Using Slicers |
| Creating Pivot tables | Creating graphs |
| Using Cell Referencing | Functions and Formulas |
| Edit Charts | VBA |
Check: Top 10 Excel Courses Online
R Programming
| Background and Nuts & Bolts | Programming |
| Loop Functions and Debugging | Simulation and Profiling |
SQL
| Basic concepts | Creating Database |
| Entity-relationship modeling | Adding Records to a table |
| Relational model | SQL Subqueries |
| Data Manipulation | SQL Injections |
Check: Top 10 SQL Certification Courses Online
Machine Learning
| Introduction to different Learning methods (Supervised, Unsupervised, and Reinforcement Learning) | Decision Tree |
| Database and SQL | Data Preprocessing and Data Mining |
| Linear Regression | Exploratory Data Analysis |
| SVM | Logistic Regression |
| CNN | Naive Bayes |
Top Data Analytics Books
| Books | Authors |
|---|---|
| The Elements of Data Mining, Statistical Learning, Inference, and Prediction | Robert Tibshirani, Trevor Hastie, Jerome Friedman |
| Data Analysis Software: Programming with R (Statistics and Computing) | John M. Chambers |
| Probability & Statistics for Engineers & Scientists | Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying Ye |
| Data Mining and Analysis | Mohammed J. Zaki, Wagner Meira |
Top Data Analytics Tools and Software
Many tools have risen with several functionalities for this purpose with the increasing demand for Data Analytics in the market. Whether it is user-friendly or open-source, the following are some of the top tools in data analytics.
- Tableau: This software enables connection to any data source for free such as Corporate Data Warehouse, Excel, etc. then it creates maps, visualizations, and dashboards with real-time updates on the web.
- QlikView: It offers in-memory data processing quickly with the results delivered to the end-users. It comes with data association and data visualization with data being compressed.
- Python: It is an open-source object-oriented programming language that is easy to read, write, and maintain. It offers several visualization and machine learning libraries like TensorFlow, Matplotlib, Scikit-learn, Pandas, Keras, etc. This tool can be fabricated on any platform like a MongoDB database, SQL server, or JSON.
- RapidMiner: This tool is a powerful integrated space that can combine with any data source type such as Microsoft SQL, Excel, Access, Tera data, Oracle, Sybase, etc. It is used for predictive analytics mostly like text analytics, data mining, and machine learning.
- OpenRefine: This tool is a data cleaning software that will help you clean up data for analysis which is also known as GoogleRefine. It is used to clean messy data for the transformation and parsing of data from websites.
- SAS: This tool is a programming language and environment for data manipulation and analytics that can be easily accessible and can analyze data from various sources.
Data Analytics Syllabus: FAQs
Ques. Can I pursue Data Analytics courses online after Class 10th?
Ans. No, you can pursue Data Analytics courses only after class 12th.
Ques. Can I pursue Data Analytics courses online?
Ans. Yes, Data Analytics courses can also be pursued from the convenience of your home through several online web portals.
Ques. Which type of Data Analytics tools are there?
Ans. The tools of Data Analytics Microsoft Excel, Tableau, Python, SQL, R, and so on.
Ques. Does Data Analytics have a good career?
Ans. According to the report of the World Economic Forum, most of the companies will be hiring Data Analysts from next year onwards. So on that note, candidates must be aware of the skills that will help them get a good position as a Data Analyst.
Ques: What is the syllabus of data analytics?
Ans: Students have to learn a wide range of subjects in the data analytics course. The course includes subjects like Data Collection, Data Visualization, Probability and Statistics, Data Structures and Algorithms, and many more.
Ques: Is learning data analysis difficult?
Ans: The best answer would be it depends. The data analysis courses require the learner to have a good understanding of the different programming languages and analytical software.
Ques: Is being a data analyst a stressful job?
Ans: Data analysts and scientists need to scour the internet and the whole database so that they can form an idea about the trends. It can be termed as a challenging job that requires dedication and patience.
Ques: What is the data analytics salary?
Ans: In India, data analysts can expect a salary of INR 420,000.
Ques: How long does it take to become a data analyst?
Ans: The UG course in Data analytics takes about 4 years to complete. Certificate and diploma courses in Data Analytics can range from a few weeks to 12 months
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