DATA SCIENCE SYLLABUS for years 2022-2023

To start the learning journey in Data science you must first know the data science syllabus thoroughly. It will not only help you to make your curriculum easier to understand but also give a road map for your thrilled learning experience. Be it Statistics and mathematics, or SQL, or Python programming, we will be going deep down the course contents and give you a complete overview of the latest data science syllabus 2022-23.

There are different courses available for Data science with various levels of understanding and application techniques, but the core subjects remain big data, Business intelligence and Machine learning. Anexas has designed a course based on these subjects, industry requirements and individual requirements

If you want to start your career in data science, enroll in any of the following courses according to your requirements and learn data science with the latest syllabus

Data Science – Basic Course

Data Science – Intermediate Course

Data Science – Advance Course

This article consists of information on Data Science topics, concepts used in it and the latest syllabus of Data Science for years 2022-2023.

DATA SCIENCE SYLLABUS for years 2022-2023
DATA SCIENCE SYLLABUS for years 2022-2023
Diagram showing the different subject domains and soft skills that a data scientist has to master to be successful in the career
Data science skills and competencies

What is data science?

Data science is a field of study in which data is assessed based on business requirements and goals, and decisions are made depending on the patterns and results of the research analysis done by data scientists. This is achieved by deep diving into the data ocean and gaining the required knowledge through scientific techniques and processes. A data scientist is a person who writes algorithms for data analysis using a combination of concepts from maths, statistics, computer sciences, and business intelligence. Data science is present all around the world and is utilised in a wide range of domains like-

  • Healthcare
  • Manufacturing industries
  • IT
  • E-commerce
  • Sales
  • Telecommunication
  • Finance

If you are equipped with a data science skill set, you will have a chance to strengthen your profession and add to the development of your organisation.

What are the subjects of data science?

The main subjects to focus on data science syllabus are

  • Statistics – Data scientists are required to make decisions based on data available or mining the required data. Statistics is all about data and its interpretation.
  • Programming Python- Programming skills are the major competencies for the data scientists; they code the required data structures and solve complex data architecture problems faced by the organisations.
  • SQL – SQLis a data mining software which makes it easy to pull out data from the ocean of data present in the database.
  • Machine learning – modern technology works on machine learning and Artificial intelligence, data scientists use these technologies for their research.
  • Data interpretation skills – The data interpretation skills is one of the most important skills required to become a data scientist.
  • Big data – Storing and managing huge amounts of data is becoming a major challenge in the modern world, big data deals with the ocean of data and its management.
  • PowerBI – PowerBI is about data representation and visualisation. After researching and finding out the solution or decision, it is important to present the data in front of key stakeholders.
  • Business communication – Business communication deals with understanding of data sharing and communication with business owners. Understanding of business decorum and organisational leadership is important.
  • Decision making – Given the data is pulled out and statistical analysis is done, it is the decision-making skill which takes understanding of business for the data scientist to arrive at a decision.

Which are different courses in data science?

There are different courses one can pursue to become a data scientist. After completing class 12th, you can take diploma courses as well as graduation courses in data science. After graduation if you are interested in pursuing data science there are post graduate programmes are also available proceeding to masters and then PhD in data science. There is huge demand for data science professionals in various industries.

  • Diploma in data science
  • Certification course in data science
  • Graduation course in data science for science students.
  • Graduation courses in data science for business students.
  • Post-Graduation in data science.
  • PhD in data science.

The syllabus of data science?

The syllabus of data science comprises computer science, data mining, machine learning, database and process, statistics, communication skills, data visualisation. These are one of the most important topics which you should focus on in the syllabus of data science year 2022-23. The focus and depth of study is based on your academic level i.e., diploma, undergraduate, graduation, post-graduation, PhD, or professional certification.

Courses Offered by Anexas Europe Our upcoming Data Science course schedule
S. No. Course Name Dates Time
1.  Basic Data Science    
2. Intermediate Data Science    
3. Advanced Data Science    
4. DATA SCIENCE (Full course)    
All the upcoming courses offered by Anexas Europe based on the latest Data science syllabus 2022-23. You can always contact our team for further information.

The syllabus of data science keeps on revising, but Anexas is well updated its course curriculum as per latest data science syllabus 2022-23

Data science syllabus in diploma?

Following are the topics covered in the basic diploma course in data science.

It is a 2year course containing 4 semesters consisting of all the basic data science topics to be taught at beginner level. The topics are:

  • Introduction to Data Science
  • Data Mining
  • Cloud Computing
  • Data Analysis
  • Data Visualisation
  • Data Model Selection and Evaluation
  • Machine Learning
  • Business Intelligence
  • Data Warehousing
  • Data Dashboards and Storytelling

Data science syllabus for graduation?

Data Science syllabus for BSc

BSc Data Science duration is 3 years. B.Sc. The Data Science Syllabus is divided into 6 semesters. The syllabus for each semester is different and includes Artificial Intelligence, Applied Statistics, Cloud Computing, along with elective subjects. The table below summarizes the BSc Data Science Syllabus semester-wise.

The topics which are covered in graduation are

  • Mathematical & Statistical Skills
  • Machine Learning
  • Coding
  • Algorithms used in Machine Learning
  • Statistical Foundations for Data Science
  • Data Structures & Algorithms
  • Scientific Computing
  • Optimization Techniques
  • Data Visualisation
  • Matrix Computations
  • Scholastic Models
  • Experimentation, Evaluation and Project Deployment Tools
  • Predictive Analytics and Segmentation using Clustering 
  • Applied Mathematics and Informatics
  • Exploratory Data Analysis
  • Business Acumen & Artificial Intelligence

Following table is showing semester wise subject in 6 semester courses:

Semester I Semester II
Linear Algebra Probability and Inferential Statistics
Basic Statistics Discrete Mathematics
Programming in C Data Structures and Program Design in C
Communication Skills in English Computer Organisation and Architecture
Programming in C Lab Data Warehousing and Multidimensional Modelling
Microsoft Excel Lab Data Structure Lab
Programming in R Lab
Semester III Semester IV
Object-Oriented Programming in Java Machine Learning, I
Database Management Systems Cloud Computing
Operating Systems Data Warehousing and Multidimensional Modelling
Design and Analysis of Algorithms Operations Research and Optimization Techniques
Database Management Systems Lab Time Series Analysis
Object-Oriented Programming in Java Lab Machine Learning, I Lab
Data Warehousing and Multidimensional Modelling Lab
Semester V Semester VI
Machine Learning II Elective I
Introduction to Artificial Intelligence Elective II
Big Data Analytics Grand Viva
Data Visualisations Major Project
Programming in Python Lab  
Big Data Lab  
Minor Project  

Data science syllabus for post-graduation?

Data Science syllabus for M.Sc.

MSc Data Science is a 2-year research-based postgraduate level program. The course revolves around Calculus, Descriptive Statistics, and C Programming, and the use of various technologies such as ML, DL, Python, and Spark are taught thoroughly.

Semester I Semester II
Mathematics for Spatial Sciences Spatial Big Data and Storage Analytics
Applied Statistics Data Mining and Algorithms
Fundamentals of Data Science Machine learning
Python Programming Advanced Python Programming for Spatial Analytics
Introduction to Geospatial Technology Image Analytics
Programming for Spatial Sciences Spatial Data Base Management
Business Communication Flexi-Credit Course
Cyber Security
Integrated Disaster Management
Semester III Semester IV
Spatial Modelling Industry Project
Summer Project Research Work
Web Analytics
Artificial Intelligence
Flexi-Credit Course
Predictive Analytics and Development

MSc Data Science Electives

Deep Learning System Dynamics Simulation
IOT Spatial Analytics Spatial User Interface Design and Implementation
Research Modelling and Implementation Genomics
Exploratory Data Analysis Multivariate Analysis
Stochastic Process Programming for Data Science in R
Hadoop Image and Video Analytics
Internet of Things Identification and Data Collection

Data science syllabus for a M. Tech in data science?

MTech in Data Science is a 2-year research-based postgraduate level program. The course revolves around Calculus, Descriptive Statistics, and C Programming, and the use of various technologies such as ML, DL, Python, and Spark are taught thoroughly.

Following is semester wise subjects to be covered during the course

Semester I Semester II
Data Science Programming Data Analytics and Graphs
Data Mining and Warehousing Empirical Research
Econometrics Advanced-Data Analytics
Construction Economy and Finance Big Data
Data Analytics Mathematics Open Elective II
Open Elective I Project
Laboratory Laboratory
Semester III Semester IV
Open Elective III Evaluation of project and Viva
Project Seminar
Open Elective IV Training and Internship

MTech Data Science Electives

Deep Learning Natural Language Processing
Artificial and Computational Intelligence Stream Processing and Analytics
Data Visualisation and Interpretation Graphs-Algorithms & Mining
Optimization Methods for Analytics Big Data Systems
Advanced Topics in Data Processing Information Retrieval
Probabilistic Graphical Models Data Warehousing
Systems for Data Analytics Ethics for Data Science

Data science syllabus for certification courses?

Professional certification courses are also available for data science where all the topics of data science are covered. Anexas is pioneer in training data science professionals the details are given below

Data Science courses with Anexas

If you are new to Data Science, Anexas is the right place to start. Learn about Data Science and all its components and become a Data Scientist. Gain knowledge from skilled industry experts who have more than 15 years of experience in the corporate market and have worked with the biggest organisations in the world. Get valuable certificates in your name and become a Data Scientist. Datamitum in collaboration with Anexas brings you a Data Science course where you can invest your time, money, and effort in the right direction

How will the Data Science course benefit you?

  • Learn to use various industry-level tools
  • Learn different disciplines of Data Science
  • Inculcate Leadership skills
  • Get international career opportunities
  • Gain advanced skill sets
  • Get better job profiles
  • Expand your Influential network
  • Apply knowledge of AI, ML, Python and NLP at your workplace
  • Learn the use of Probability and other statistical tools in projects
  • Get insights of Deep Learning

Why to Choose Anexas?

With Anexas we are providing world class training, taking into consideration the ongoing demand of data science in top industries. Following are the key features of our courses:

  • 135 hours of interactive online live training with industry experts
  • Content-rich study material
  • Free eBooks
  • Projects for better understanding
  • Assignments for each module
  • Participants will be certified by ANEXAS Europe
  • International recognition
  • Tools required for the training
  • Open-book online test for certification after each module
  • 100% Pass rate guaranteed
  • Lifetime membership to Anexas Alumni group
  • Mock Interviews for job preparations
  • Guidance for resume-building
  • Get a chance to win cashback benefits
  • Online Line Customer support 24*7

Syllabus of data science courses with Anexas

Anexas offers a comprehensive course for future ready professionals. The Course is divided into three levels i.e., Basic, Intermediate, and advanced.

The details of all the subjects are listed below: .

Data Science course: Basic Level

The basic level course content has all the important topics that are required for students who are starting the data science course and want to learn from basics.

  Level 1 – Basic  
Delivery Topics Delivery Mode
Python Installation – Anaconda, PyCharm, Virtual environment Live Code – Jupyter/Colab/PyCharm + Slides
Introduction to python
Basic Syntax, comments, Variables
Data Types, Numbers, Casting, Strings, Booleans
Operators, Lists, Tuples, Sets, Dictionaries
 If…Else, While Loops, For Loops
 Functions, Lambda, Arrays
Arrays, Classes/Objects, Inheritance, Iterators
Scope, Modules, Dates, Maths, JSON
PIP, Try…Except, User Input P, String Formatting
 File Handling, Read Files, Write/Create Files, Delete Files
NumPy ND array, Data types, Array Attributes, Indexing and Slicing Live Code – Jupyter/colab/PyCharm + Slides
Array manipulation, Binary operator, String Function
Arithmetic, Statistical, Matrix, linear algebra, sort, search, counting
Pandas Data manipulation, Viewing, selection, grouping, merging, joining, concatenation Live Code – Jupyter/colab/PyCharm + Slides
Working with text data, visualisation, CSV, XLSX, SQL data pulling, operations
SciPy Statistics, Linear algebra, models, special functions, optimization Live Code – Jupyter/colab/PyCharm + Slides
Probability & Stats Applications
Probability Basic Probability, Random experiments, conditional Probability, Independent Events, Mostly Live numerical solving + Slides + mathematical intuition + codes
Bayes theorem, Permutation, combination
Random variable, Discrete/Continuous RV, PDF, PMF, CDF
Joint Probability Distribution, Conversion techniques, EV, variance, SD
Covariance, Correlation, Chebyshev Inequality, Law of Large number
Central limit Theorem, Percent & Quantiles, Moments
Skewness & Kurtosis, Gaussian, Binomial, Standard Normal, Distribution
poison, Multinomial, Hypergeometric, Uniform, Exponential Distribution
Statistics [Mean, median, mode] (Sample/population), Expected values, variance, standard deviation Mostly Live numerical solving + Slides + mathematical intuition + codes
Sampling distribution, Frequency distribution, Estimation Theory
confidence interval, Maximum Likelihood Estimation
Hypothesis Testing – Chi Square, Student’s T, F Distribution, Z test
Hypothesis Testing – Type-I, Type- II, p Values, Relationship between NULL & Alternative
Least Square Methods – Numerical
Data Pre-Processing Data Cleaning – Handling Missing Values (Data Imputation), Dealing with Noisy data (Binning Technique) Live Code – Jupyter/colab/PyCharm + Slides
Advance Data cleaning – Will be referred while Regression, clustering topics
Data Transformation Techniques- Normalisation (minmax, log transform, z-score transform etc.), Attribute Selection, Discretization, Concept Hierarchy Generation
Data Reduction: Data Cube Aggregation, Numerosity Reduction, Dimensionality Reduction
Data Visualisation Data Mapping, Charts, Glyphs, Parallel Coordinates, Stacked Graphs Live Code – Jupyter/colab/PyCharm + Slides
Bar, Pie, Line Charts, bubbles, geo maps. Gauge, whisker charts, Heatmaps, scatterplots, plotting images, videos, motion charts, performing EDA
Building Dashboard – Live implementation – PowerBI
ML Algos  
Linear Regression Implementation of Numerical intuitions Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
 Regression basics: Relationship between attributes using Covariance and Correlation
Relationship between multiple variables: Regression (Linear, Multivariate) in prediction.
Residual Analysis: Identifying significant features, feature reduction using AIC, multicollinearity
Multiple Linear Regression Polynomial Regression Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
Regularisation methods
Lasso, Ridge and Elastic nets
Categorical Variables in Regression
Non-Linear Regression Logit function and interpretation Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
Types of error measures (ROCR)
Logistic Regression in classification
Clustering Distance measures – Euclidean distance Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
Different clustering methods (Distance, Density, Hierarchical)
Iterative distance-based clustering;
Dealing with continuous, categorical values in K-Means
Constructing a hierarchical cluster
K-nearest neighbours, K-Medoids, k-Mode and density-based clustering
BIRCH, DBSCAN, Mean Shift, Spectral Clustering, Gaussian Mixture Model
Association Rule mining The applications of Association Rule Mining: Market Basket, Recommendation Engines, etc. Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding      
A mathematical model for association analysis; Large item sets; Association Rules
Apriori: Constructs large item sets with mini sup by iterations; Analysis discovered association rules;
Application examples; Association analysis vs. classification
FP-trees, PageRank

Data Science course: Intermediate Level

Data science course at intermediate level contains deeper knowledge and topics about Database, NLU, and statistics.

    Level 2 – Intermediate  
Delivery Topics Delivery Mode
Classification Naïve Bayes Classifier: Model Assumptions, Probability estimation Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
Required data processing, M-estimates, Feature selection: Mutual information
Random Forest Algo + Implementation 
classification using Logistics, K nearest Neighbours
Decision Trees: ID4, C4.5, CART
Support Vector Machines: Linear learning machines and Kernel space, Making Kernels and working in feature space
SVM for classification and regression problems.
Feature Engineering Feature Reduction/Dimensionality reduction Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
Principal components analysis (Eigenvalues, Eigen vectors, Orthogonality)
Validation Techniques (Cross-Validations)
Ensembles methods Bagging & boosting and its impact on bias and variance Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
C5.0 boosting
Gradient Boosting Machines and XGBoost
Database Build Dataset from large database Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
SQL queries & Protocol Building
Creating Feature Store using SQL
in-Depth PostgreSQL
Introduction to Statistical NLP Techniques Introduction to NLP Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
Pre-processing, NLP Tokenization, stop words, normalisation, stemming and lemmatization
Pre-processing in NLP Bag of words, TF-IDF as features
Language model probabilistic models, n-gram model and channel model
Hands on NLTK
POS Tagger
NLP- NLU Delivery Introduction to sequential models
Introduction to RNN
Intro to LSTM
LSTM backprop through time
Hands on keras LSTM
Sentiment Analysis
Sentence generation
Machine translation
Advanced LSTM structures
Keras- Machine Translation
Encoder decoder with attention
Encoder Decoder – Auto Encoder
Understanding transformers
Attention Models Intuitions
Introduction to BERT
Chatbot -hands-on

Data Science course: Advanced Level

The advanced level in data science contains all the topics which will groom you to become an expert in data science which is required by top companies today.

  Level 3 – Advanced  
Delivery Topics Delivery Mode
Neural Networks Using TensorFlow and Keras Basic Mathematics – DL Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
Introduction to Perceptron & History of Neural networks
Activation functions
a. Sigmoid
b. Relu
c. SoftMax
d. Leaky Relu
e. Tanh f. Exponential Linear Units (ELU) g. Swish
Gradient Descent
Learning Rate and tuning
Optimization functions
Introduction to TensorFlow
Introduction to keras, Theano, pytorch – hands-on
Back propagation and chain rule
Fully connected layer
Cross entropy
Weight Initialization
coding perceptron
    Computer Vision  
Working with images & CNN Building Blocks Working with Images Introduction Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
Working with Images – Digitization, Sampling, and Quantization
Working with images – Filtering – OpenCV
Hands-on Python Demo: Working with images
Introduction to Convolutions
2D convolutions for Images
Convolution – Backward – hands-on
Transposed Convolution and Fully Connected Layer as a Convolution – hands-on
Pooling: Max Pooling and Other pooling options – practical
CNN Architectures and Transfer Learning CNN Architectures and LeNet Case Study Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
Case Study: AlexNet
Case Study: ZFNet and VGGNet
Case Study: GoogleNet
Case Study: ResNet
Transfer Learning Principles and Practice
Hands-on Keras Demo: SVHN Transfer learning from MNIST dataset
Transfer learning Visualisation (run package, occlusion experiment)
Hands-on demo -T-SNE
Hands -on CNN nets
Hands-On OCR, Face Recognition, Object Detection, Pose Estimation, 3D estimations
CNNs at Work – Semantic Segmentation CNNs at Work – Semantic Segmentation Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
Semantic Segmentation process
U-Net Architecture for Semantic Segmentation
Hands-on demo – Semantic Segmentation using U-Net
Other variants of Convolutions
Inception and MobileNet models
Object Detection CNNs at Work – Object Detection with region proposals Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
CNNs at Work – Object Detection with Yolo and SSD 
CNNs at work- Siamese Network for Metric Learning Siamese Network as metric learning Live Code – Jupyter/colab/PyCharm + Slides + Real time Use cases coding
How to train a Neural Network in Siamese way
Hands-on demo – Siamese Network


“The world’s most valuable resource today is no longer gold or oil, but data.” In this digitally driven economy, Data is extremely valuable and the key to the smooth functioning of every organisation as it helps take quicker and better decisions. The Data Science domain is expected to see 250,000 new openings in 2022, an increase of 62% compared to 2021.There is a huge opportunity in the field of data science and Anexas is with you to make your industry ready with our course.

If you think this is too much information to take and how you are going to keep up with the changing syllabus, you don’t need to worry. Take this free introductory session on Data Science and learn how Anexas provides lifetime access to course content and recorder video when you join the course. 


Does data science Certification increase your salary?

Yes, Data science Certification will increase your salary. A survey conducted across companies showed that the Data science professionals get 40% hike in the salary

Is the Data science certification worth it?

Yes, Data science certification is worth it. Data science teaches you tools and techniques that will give you a huge advantage to develop your data analytics skills. It also opens many doors for your career opportunity.

Is it compulsory to have a bachelor’s degree to pursue data science?

No, Data science can be done after 12th by certification courses or diploma in data science.

Will Data science help my career?

Yes, Data science will help your career as it teaches you knowledge that can be applied in any projects irrespective of departments. Since Data science is recognized globally you can apply for companies all over the globe.

Which is better – Data analytics or Data science?

Data science is better than data analyst as it consists of all the knowledge data analysts have. So why Soldier when you can become a General.

Do I need to learn advanced level programming for data science?

No, Data science involves an intermediate level of programming skills

Also read: Business Analytics vs Data Science

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