Data Analyst Course Syllabus: Fees, Duration, & Eligibility

Find the complete Data Analyst course syllabus and curriculum for the 2024-2025 academic session in India. I have covered branches such as B.Sc, M.Sc, M.Tech, and more.

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Data Analyst course syllabus and curriculum 2024

Here’s a Data Analyst course syllabus at a glance:

Module 1: Data Fundamentals

Introduction to Data Analysis

• Definition of Data Analysis
• Types of Data Analysis

Data Types and Structures

• Types of Data (Quantitative, Qualitative, Categorical)
• Data Structures (Arrays, Lists, Dictionaries)
• Data Formats (CSV, Excel, JSON)

Exploratory Data Analysis Techniques

• Summary Statistics (Mean, Median, Mode)
• Data Visualization (Histograms, Box Plots, Scatter Plots)
• Data Cleaning and Preprocessing

Data Visualization Basics

• Introduction to Data Visualization
• Types of Visualizations (Tables, Charts, Maps)
• Best Practices for Data Visualization

⭐ Hands-on projects to practice: 

• Analyze a dataset of your favorite sports team’s performance over the past season.
• Explore a publicly available dataset and perform exploratory data analysis to identify patterns, correlations, and outliers.
• Create a dashboard using Tableau or Power BI to visualize a dataset of your choice.

Module 2: Business Statistics

Descriptive Statistics

• Measures of Central Tendency (Mean, Median, Mode)
• Measures of Variability (Range, Variance, Standard Deviation)
• Data Visualization for Descriptive Statistics

Inferential Statistics

• Hypothesis Testing (Null and Alternative Hypotheses)
• Confidence Intervals (Point Estimates, Interval Estimates)
• Types of Errors (Type I, Type II)

Regression Analysis

• Simple Linear Regression
• Multiple Linear Regression
• Regression Analysis Assumptions

Hypothesis Testing and Confidence Intervals

• Hypothesis Testing for Means and Proportions
• Confidence Intervals for Means and Proportions
• Non-Parametric Tests

⭐ Hands-on projects to practice: 

• Conduct a hypothesis test to determine whether there is a significant difference in the average salary of men and women in a particular industry.
• Analyze the relationship between two variables using regression analysis.
• Create a confidence interval to estimate the population mean of a characteristic.

Module 3: Microsoft Excel

Introduction to Excel

• Basic Excel Operations (Creating and Editing Worksheets)
• Excel Formulas and Functions (SUM, AVERAGE, COUNT)
• Data Management (Sorting, Filtering, Grouping)

Data Management and Analysis

• Data Validation and Error Handling
• Data Analysis Tools (PivotTables, Charts, Conditional Formatting)
• Advanced Excel Formulas and Functions (VLOOKUP, INDEX/MATCH)

Data Visualization using Excel

• Creating Charts and Graphs
• Customizing Charts and Graphs
• Best Practices for Data Visualization in Excel

Advanced Excel Topics

• Macros and VBA
• Power Query and Power Pivot
• Excel Add-ins and Plugins

⭐ Hands-on projects to practice: 

• Create a budget template for a personal or fictional business using Excel formulas and functions.
• Analyze a dataset of sales data and create pivot tables, charts, and dashboards to identify trends and insights.
• Develop a forecasting model using Excel’s built-in functions to predict future sales or revenue.

Module 4: SQL

Introduction to SQL

• Basic SQL Syntax (SELECT, FROM, WHERE)
• SQL Data Types (Integer, String, Date)
• SQL Functions (SUM, COUNT, AVG)

Data Definition Language (DDL)

• Creating and Modifying Tables
• Indexing and Constraints
• Views and Stored Procedures

Data Manipulation Language (DML)

• Inserting, Updating, and Deleting Data
• Transactions and Locking
• SQL Injection and Security

Data Query Language (DQL)

• Selecting and Filtering Data
• Joining and Aggregating Data
• Subqueries and Window Functions

⭐ Hands-on projects to practice:

• Create a database schema and populate it with sample data using SQL.
• Analyze a publicly available database and write SQL queries to answer business questions.
• Design and implement a data warehouse using SQL to store and analyze data from multiple sources.

Module 5: Data Visualization

Introduction to Data Visualization

• Importance of Data Visualization
• Types of Visualizations (Tables, Charts, Maps)
• Best Practices for Data Visualization

Tableau Fundamentals

• Connecting to Data Sources
• Creating and Customizing Visualizations
• Using Dimensions and Measures

Advanced Tableau Topics

• Using Calculated Fields and Parameters
• Creating Dashboards and Stories
• Using Advanced Visualization Tools (e.g. treemaps, scatter plots)

Data Visualization with Power BI

• Introduction to Power BI
• Creating and Customizing Visualizations
• Using DAX Formulas and Measures

⭐ Hands-on projects to practice: 

• Create a dashboard using Tableau to visualize a dataset of your choice.
• Analyze a publicly available dataset and create visualizations to identify trends and insights.
• Design and implement a data visualization project using Power BI.

Module 6: Machine Learning

Introduction to Machine Learning

• Definition of Machine Learning
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
• Machine Learning Workflow

Supervised Learning

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests

Unsupervised Learning

• Clustering (K-Means, Hierarchical)
• Dimensionality Reduction (PCA, t-SNE)
• Anomaly Detection

Advanced Machine Learning Topics

• Neural Networks and Deep Learning
• Natural Language Processing (NLP)
• Recommendation Systems

⭐ Hands-on projects to practice: 

• Build a supervised learning model to predict a continuous outcome variable.
• Implement an unsupervised learning algorithm to identify clusters in a dataset.
• Develop a recommendation system using collaborative filtering.

Module 7: Data Mining

Introduction to Data Mining

• Definition of Data Mining
• Data Mining Process
• Data Mining Techniques

Data Preprocessing

• Data Cleaning and Handling Missing Values
• Data Transformation and Feature Engineering
• Data Reduction and Dimensionality Reduction

Pattern Discovery

• Association Rule Mining
• Clustering and Classification
• Regression and Prediction

Evaluation and Deployment

• Evaluating Data Mining Models
• Deploying Data Mining Models
• Ethics and Privacy in Data Mining

⭐ Hands-on projects to practice: 

• Analyze a dataset and identify patterns using data mining techniques.
• Develop a data mining model to predict customer churn.
• Evaluate the performance of a data mining model using metrics such as accuracy and precision.

Module 8: Big Data

Introduction to Big Data

• Definition of Big Data
• Characteristics of Big Data (Volume, Velocity, Variety)
• Big Data Ecosystem

Big Data Storage and Processing

• Distributed File Systems (HDFS)
• NoSQL Databases (HBase, Cassandra)
• Big Data Processing Frameworks (MapReduce, Spark)

Big Data Analytics

• Big Data Analytics Tools (Hive, Pig)
• Big Data Machine Learning (Mahout, Spark MLlib)
• Big Data Visualization (Tableau, Power BI)

Big Data Case Studies

• Real-world examples of Big Data applications
• Big Data challenges and limitations
• Future of Big Data

⭐ Hands-on projects to practice: 

• Analyze a large dataset using big data tools and techniques.
• Develop a big data analytics project to predict customer behavior.
• Design and implement a big data architecture for a real-world application.

Module 9: Cloud Computing

Introduction to Cloud Computing

• Definition of Cloud Computing
• Cloud Computing Models (IaaS, PaaS, SaaS)
• Cloud Computing Benefits and Challenges

Cloud Infrastructure

• Virtualization and Containerization
• Cloud Storage and Networking
• Cloud Security and Compliance

Cloud Data Services

• Cloud-based Data Warehousing (Amazon Redshift, Google BigQuery)
• Cloud-based Data Integration (AWS Glue, Google Cloud Dataflow)
• Cloud-based Machine Learning (AWS SageMaker, Google Cloud AI Platform)

Cloud Case Studies

• Real-world examples of cloud computing applications
• Cloud computing challenges and limitations
• Future of Cloud Computing

⭐ Hands-on projects to practice: 

• Deploy a cloud-based data warehouse using Amazon Redshift.
• Develop a cloud-based data integration pipeline using AWS Glue.
• Design and implement a cloud-based machine learning model using Google Cloud AI Platform.

Module 10: Cybersecurity

 Introduction to Cybersecurity

• Definition of Cybersecurity
• Types of Cybersecurity Threats (Malware, Phishing, Ransomware)
• Cybersecurity Best Practices

Network Security

• Network Fundamentals (TCP/IP, DNS, DHCP)
• Network Security Protocols (SSL/TLS, SSH)
• Network Security Threats (DDoS, SQL Injection)

Data Security

• Data Encryption (AES, RSA)
• Data Backup and Recovery
• Data Loss Prevention (DLP)

Cybersecurity Case Studies

• Real-world examples of cybersecurity breaches
• Cybersecurity challenges and limitations
• Future of Cybersecurity

⭐ Hands-on projects to practice: 

• Implement a network security protocol using SSL/TLS.
• Develop a data encryption algorithm using AES.
• Design and implement a cybersecurity awareness program for a fictional organization.

Module 11: Artificial Intelligence

Introduction to Artificial Intelligence

• Definition of Artificial Intelligence
• Types of Artificial Intelligence (Narrow, General, Super)
• Artificial Intelligence Applications

Machine Learning Fundamentals

• Supervised Learning (Regression, Classification)
• Unsupervised Learning (Clustering, Dimensionality Reduction)
• Reinforcement Learning

Deep Learning

• Introduction to Deep Learning
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

Natural Language Processing

• Introduction to Natural Language Processing
• Text Preprocessing (Tokenization, Stemming, Lemmatization)
• Sentiment Analysis and Text Classification

⭐ Hands-on projects to practice: 

• Develop a machine learning model to classify handwritten digits using MNIST dataset.
• Implement a natural language processing algorithm to perform sentiment analysis on a dataset of movie reviews.
• Design and implement a chatbot using natural language processing and machine learning.

B.SC Data Analyst syllabus

The BSc (Hons) in Data Analytics is a 3-year undergraduate (UG) program that equips students with a solid foundation in data analysis techniques and tools.

The average fees for the BSc (Hons) in Data Analytics course range from INR 30,000 to INR 2,70,00 per annum, depending on the college and location.

Here’s the BSC Data Analyst course curriculum:

M.Sc Data Analyst Syllabus

The M.Sc. in Data Analytics is a 2-year postgraduate program focused on advanced data analysis, statistical modeling, and data-driven decision-making. 

This program is designed for graduates with a relevant background in computer science, mathematics, or engineering.

Diploma Data Analyst Syllabus:

To be eligible for Diploma Data Analyst courses, aspiring students typically need to hold a bachelor’s degree in a relevant field such as statistics, information technology, economics, computer science, or mathematics with a minimum of 60% overall or equivalent CGPA.

M.Tech Data Analytics Curriculum:

M.Tech Data Analyst Syllabus The M.Tech in Data Analyst is a 2-year postgraduate program designed to provide students with a strong foundation in data analysis principles, tools, and applications.

To be eligible for the M.Tech in Data Analyst program, candidates must have:

•  A bachelor’s degree in Computer Science, Information Technology, Statistics, Mathematics, or a related field with a minimum of 50-60% marks. 
• A valid GATE score in Computer Science, Statistics, or a related discipline. Qualifying in an interview conducted by the university.

Data Analyst course subjects and topics to learn

Data structure and algorithms 

Data structures and algorithms play a great role in data processing and analysis. They allow you to organize, manage, and process data efficiently and ensure better data retrieval and manipulation. For example, hash tables and binary search tree data structures help in faster data search and updates, which is essential for real-time data analytics. 

Some essential topics of data structures and algorithms include arrays, lists, stacks, queues, iteration, recursion, hash tables, binary search trees, searching, and sorting. 

Probability and Statistics 

Knowledge of probability and statistics in mathematics is important for data analysis. It is essential for data interpretation, hypothesis testing, and building predictive models, which help analysts interpret data accurately and make better predictions of future events. 

Some primary areas to focus on include probability and probability distribution, sampling distributions, estimation and hypothesis testing, data cleaning, imputation techniques, and correlation and regression. 

A few courses cover business statistics that includes statistical analysis, and data analytics together. 

Data collection and cleaning 

You often work with raw data that is messy and incomplete. Therefore, learning how to collect and clean that data is essential to ensure accurate and reliable analysis. Focus on data collection methods, data cleaning techniques, and data processing to ensure the data you work with is of high quality and can provide valid outputs. 

This includes learning about survey sampling, observational results, statistical techniques, analysis of unstructured data, and extracting and presenting statistics.

Microsoft Excel 

You need knowledge of spreadsheet software like Excel to sort, filter, and manage data and perform complex calculations.

This includes learning basic concepts like text-to-columns, concatenation, absolute cell references, data validation, conditional formatting, using the IF function, pivot tables, filtering and sorting a power table, Macros, and VBA programming for automating data analysis tasks. 

Database management

Data is often stored in databases, which makes understanding how to manage and query these databases essential. This helps you access and manipulate data effectively and efficiently for data storage and retrieval. 

Some basic things to know in database management are the relational database concepts, SQL for querying databases, database designing and normalization, and knowledge of NoSQL databases. 

Tableau and PowerBI 

Both these software are essential for data visualization, which helps you understand complex data and communicate insights clearly. Tableau can analyze data efficiently, and PowerBI can convert raw data collected from spreadsheets, the cloud, or a data warehouse into valuable insights.

Python programming language 

Python programming skills are necessary for data analysts to automate different tasks, perform advanced data manipulation, and develop custom solutions. You need to learn the basics of Python, including its data structures and data types, string operations, operators, functions, control flow, and error and exception handling.

Knowledge of Python Libraries 

Python provides multiple data manipulation, analysis, visualization, and machine learning libraries like Matplotlib, Seaborn, Pandas, NumPy, TensorFlow, Scikit-Learn, Keras, and more. This makes the data analysis process easy and smooth.

For example, Pandas is used for data cleaning and transformation, performing operations like margin, joining and reshaping data, and implementing exploratory data analysis (EDA).

Similarly, Matplotlib is used for creating static, animated, and interactive visualizations. They can generate high-quality plots for reports and presentations.

R programming 

Having knowledge of R programming is also good for data analysts. It helps in statistical computing and graphics. You need to learn the basic concepts like fundamentals of R programming and its data structures, variables, data types, and vector operations, manage and manipulate the data structures, and implement statistical methods and data visualization techniques,

Advanced data analysis techniques 

You must know the four main types of data analysis: descriptive, predictive, diagnostic, and prescriptive. 

In descriptive analytics, you need to learn about data aggregation and data mining. These allow you to gather data and present it in a summarized format, then mine that data to discover hidden patterns.

In diagnostic analytics, you will learn to identify anomalies in data. Then to predict the future, and get actionable, data-driven insights, you must implement predictive analytics techniques. Finally, using prescriptive analytics you can advise on what actions and decisions to take based on the predictions. 

Two other data analysis techniques to know are Exploratory Data Analysis and Time Series Data Analysis. 

Machine Learning basics

Some advanced-level data analytics courses include Machine Learning concepts. ML algorithms are designed to feed machines with data and utilize them for conducting independent research. You need to learn their basic concepts to develop predictive models that automate the data-driven decision-making process.

Explore top Data Analytics Courses In Pune and Data Analytics Courses in Hyderabad to learn the skills needed to turn data into insights and make smarter decisions.

Data Analyst course fees and duration 2024

What is the course fee of Data Analyst courses? 

The course fee for Data Analyst courses ranges between ₹10,000 to ₹85,500. However, this fee structure may vary depending on different reasons like the type of course, institutional offering, reputation of the institute, and its location. For example, institutes in tier-1 cities of India charge more than tier-2 and tier-3 cities.

Similarly, if you are doing a certificate or diploma course in data analyst, it will be cheaper than UG and PG level courses.

Data Analyst Course Duration 

Data Analyst course usually extends from 3 months to 1 year or more. It varies with the course syllabus, type of course, number of practical classes, and hours of training. If you choose a certificate course, it will usually be between 3 to 6 months.

However, self-paced courses give you the flexibility to choose your learning hours and complete the course according to your learning abilities. 

Who is eligible for Data Analyst courses?

If you want to enroll in any online training course for Data Analyst, there are no such criteria or eligibility. However, having a basic understanding of computers, programming concepts, and data structures will be helpful.

For academic courses in India: Students are eligible for Artificial Intelligence courses after completing their 12th grade, with specific criteria depending on the course type:

• Diploma in Data Analyst: A bachelor’s degree in any relevant field, with a minimum of 60% overall or equivalent CGPA.
• B.Sc in Data Analyst: Eligible for students who have completed 10+2 with Physics, Mathematics, Chemistry also need at least 50% marks.
• Postgraduate Courses: A bachelor’s degree in IT or related fields is necessary, with a minimum of 50% marks required.

Somrita Shyam

Somrita Shyam is a content writer with 4.5+ years of experience writing blogs, articles, web content, and landing pages in multiple domains. She holds a master’s degree in Computer Application (MCA) and is a Gold Award winner at Vidyasagar University. Her knowledge of the tech industry and experience in crafting creative content helps her write simple and easy-to-understand tech pieces for readers of all ages. Her interest in content writing began after helping PhD scholars in submitting their assignments. Later in 2019, she started working as a freelance content writer at Write Turn Services, and has worked with numerous clients, before joining Experlu (An UK based accounting firm) in 2022 and working as a full-time content writer in GigDe (2022-2023).