Best Institute for Data Science Certification Course with Placement Training Classes
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Data Science Course Syllabus
Build Strong Skills in Python, Statistics, Machine Learning
Data Science
- What is Data Science? Use cases with Business Problem (Mobile/Banking) and How ML gives a solution, Types of Roles, what learnings are important, VAC courses offers, Jumbo Pass, Q & A.
- ML Project Life Cycle(Problem, Collecting the data, EDA,Cleaning,Transformation, Partition, Model fitting, Cross validation, Metrics, Deployment)
- Sample, population, Data types(continous, discrete), Central tendency, spread, shape of the data such histogram, skewness, kurtosis
- Bargraph, Box plot(IQR, Whisker lengths, outliers), Scatter plot( Positive , Negative, Neutral), correlation
- Intro to Python language,Anaconda Installation(Jupyter, Spyder), Datatypes(Int, Float,dic,Set), operators(Arthemetic,comparision,Logical, Assignment)
- Data structures(List (types of list methods such as append ,extend ,insert ,remove ,pop ,clear ,index ,count ,sort ,reverse), tuples,dictionary,set), What are Control structures (if, ifelse, if elif, Nested if)
- For loop, functions, numpy(scalar,array, vector, 1 dim, 2 dim, random int), converting numpy to pandas, giving column names, Importing pandas, (read_csv, head, tail, describe)
- Pandas (info, selecting columns, dropping columns, groupby, concat(row and columns),merge, removing duplicates, filling blanks with mean)
- EDA (showing graphs such as histogram, boxplot, bargraph, scatter plot, heat map using matplotlib, seaborn) using Google collab with generative AI usage. Giving an example dataset ask them to work in class
- Probability, Normal distribution theory, standardization, zscore, z tables, applications, python code, confidence Interval
- Level of significance, Hypothesis Testing (One sample Z test, Two sample Z test), t-test
- Simple Linear Regression, metrics such RMSE and R square - Working on Age vs Weight example
- Intro to Regression models , MLR - Assumptions of Linear Regression, Variable selection, Multicollinearity VIF
- what is meant by classification models ? When do we choose Logistic regression, modelfitting, confusion matrix, accuracy score - Working on Breast cancer case study
- Other metrics Sensitivity, Specificity, precision , F1 score, ROC curve, AUC score
- Data Transformation(Standardard scaler, minmax scaler, label encoding, one hot encoding) and Data partition (Training and Test)
- Cross validation (Stratified K-Fold, K-Fold cross validation,Shuffle Split Cross-Validation)
- Variance Biased Trade-off(under fitting-causes-Lack of training , best fit, over fitting - causes -Noise in training data,Too many training epochs or iterations, too many variables) ,Visualizations (Underfitting ,bestfit, Overfitting) and Feature Engineering - Working on Bangalore housing prices case study
- Techniques such Lasso, Ridge, ElasticNet - Working on "Banglore housing prices" case study.
- Support vector machine (Hyperplane, Maximum margin classifier, Support Vectors, SVM for Linear Classification , SVM for Non-Linear Classification(polynomial, RBF, Sigmoid)
- Decision Tree Structure(Root node,Internal nodes,terminal nodes),Gini Impurity, Entropy and Information Gain (for classification), Overfitting and Underfitting in Decision Trees, Pruning,Hyperparameters - Working on Sales data set using python
- Ensemble Methods: Bagging and Random forests , working on hyper parameters to control overfitting.
- Sequential methods: Gradient Boosting, Ada Boost, using Grid search CV
- XG Boost, LightGBM
- Final project with Deployment
- What are DImensional Reduction Techniques ? 1. Purpose of PCA 2. Eigenvectors/Eigen values 3. Applications 4. Advantages 5. Working on case study
- Introduction to Clustering, Distance Metrics,Clustering Algorithms(K mean, dbscan),Choosing the Right Number of Clusters(Elbow Method,Silhouette Analysis)
- what is Recommendation and why it is important? What is Collaborative Filtering (CF) And Content-Based Filtering ?
- Time series Concepts, components, Visualization,Data partition, Lagplot, ARIMA models,Python code on ARIMA models
- Perceptron , Single Layer Network, activation functions, Back propagation method, Simple ANN code
- Multilayer Neural network, Gradient Descent method, optimizers, learning rate - complete code with tensorflow
- RNN - use cases, vanishing and exploiding problem, Simple RNN code
- LSTM Architecture, Working model, LSTM vs GRU, python code
- What is Text Data,Various forms,Applications, Tex pre-processing(Tokenization,Normalization,Stopwords,Lemmatization,stemming), Visualization on preprocessed text data
- Text Representation: Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Sentiment Analysis, Classification model using ML
- Named Entity Recognition (NER), What is Word Embedding?
- What are pre-trained word Embeddings, Word2Vec(Skip gram, CBOW), real time applications, example codes
- Language Modeling: N-gram Models, Neural Language Models, applicaton of RNNs, LSTMs on Text data
- Large Language Models? Transfer Learnings in NLP, what are pre-trained models?
- what are tansformers? Hugging Face transformers library and its use cases
Core Python
- Python Introduction - Programing Cycle of Python, Python Installation, Python IDE Variables , Data types
- Operator -Arthmatic ,Comparison , Assignment ,Logical , Bitwise opeartor, List, Tuple, Set, Dictironary
- Conditional Statements (if, if-else, if elif, Nested if), Loops in Python (for, while), Loop Control Statements(break, continue, pass)
- Function - Define function , Calling function, pass by refernece as value , Function arguments , Anonymous functions , return statements Scope of variables - local & global,Lambda, map, filter, reduce
- Importing modules, Creating user-defined modules, Python Standard Library,Installing packages using pip
- Importing the data,Handling Missing Data: ,Filtering Out Missing Data ,Filling In Missing Data ,Data Transformation ,Removing Duplicates
- Data Type Conversion, Detecting outliers using Boxplot, Z score, Handling Outliers (Capping,Transformation,Removal),
- Transforming Data Using a Function or Mapping ,Replacing Values , Feature Engineering such as Creating new variables ,Aggregations and groupings
- Hierarchical Indexing,Combining and Merging multiple datasets (merge(), join(), concat()),Reshaping and Pivoting
- Convert to datetime ,Extract attributes ,Create datetime range ,Resample data ,Time delta calculations ,Add time offset ,Time zone conversion ,Set datetime index ,Filter by date ,Handle missing time data
- 1. Exception Handling : Try, except, else, finally ,Built-in exceptions ,Raising exceptions ,Custom exceptions ,Hands-on error handling tasks
- 2. Regular expressions: match function , search function , matching vs searching Regular exp modifiers and patterns
- Class and Object, __init__ method , Attributes and methods, Hands-on: Create simple classes
- Inheritance,Polymorphism,Hands-on: Real-world OOP examples
- Encapsulation and Abstraction,Hands-on: Real-world OOP examples
- Iterators and Generators, Decorators
Tableau
- What is Tableau ?
- What is Data Visulaization ?
- Tableau Products
- Tableau Desktop Variations
- Tableau File Extensions
- Data Types, Dimensions, Measures, Aggregation concept
- Tableau Desktop Installation
- Data Source Overview
- Live Vs Extract
- Overview of worksheet sections
- Shelves
- Bar Chart, Stacked Bar Chart
- Discrete & Continuous Line Charts
- Symbol Map & Filled Map
- Text Table, Highlight Table
- Formatting: Remove grid lines, hiding the axes, conversion of numbers to thousands, millions, Shading, Row divider, Column divider Marks Card
- What are Filters ?
- Types of Filters
- Extract, Data Source, Context, Dimension, Measure, Quick Filters
- Order of operation of filters
- Cascading
- Apply to Worksheets
- Need for calculations
- Types: Basic, LOD's, Table
- Examples of Basic Calculations: Aggregate functions, Logical functions, String functions, Tablea calculation functions, numerical functions, Date functions
- LOD's: Examples
- Table Calculations: Examples
- What is Data Combining Techniques ?
- Types
- Joins, Relationships, Blending & Union
- Dual Axis
- Combined Axis
- Donut Chart
- Lollipop Chart
- KPI Cards (Simple)
- KPI Cards (With Shape)
- What are Groups ? Purpose
- What are Bins ? Purpose
- What are Hierarchies ? Purpose
- What are Sets ? Purpose
- What are Parameters ? Purpose and examples
- Reference Lines
- Trend Line
- Overview of Dashboard: Tiled Vs Floating
- All Objects overview, Layout overview
- Dashboard creation with formatting
- Actions: Filter, Highlight, URL, Sheet, Parameter, Set
- How to save the workbook to Tableau Public website ?
Mysql
- Introduction to Databases, Introduction to RDBMS, Explain RDBMS through normalization, Different types of RDBMS , Software Installation(MySQL Workbench)
- Types of SQL Commands (DDL,DML,DQL,DCL,TCL) and their applications Data Types in SQL (Numeric, Char, Datetime)
- SELECT:LIMIT,DISTINCT,WHERE AND,OR,IN, NOT IN,BETWEEN, EXIST, ISNULL ,IS NOT NULL,Wild Cards, ORDER BY
- Usage of Case When then to solve logical problems and handling NULL Values (IFNULL, COALESCE)
- Group By, Having Clause. COUNT, SUM,AVG,MIN, MAX, COUNT String Functions, Date & Time Function
- NOT NULL, UNIQUE, CHECK, DEFAULT, ENUM, Primary key,Foreign Key (Both at column level and table level)
- Inner, Left, Right, Cross, Self Joins, Full outer join
- DDL: Create, Drop, Alter, Rename, Truncate, Modify, Comment
- DML: Insert, Update & Delete TCL: Commit, Rollback, Savepoint and Data Partitioning
- Indexes (Different Type of Indexes) and Views in SQL
- Stored Procedures - Procedure with IN Parameter, Procedure with OUT parameter, Procedure with INOUT parameter
- User Define Function, Window Functions - Rank, Dense Rank, Lead, Lag, Row_number
- Union, Union all,Intersect, Sub Queries, Multiple Query
- Handling Exceptions in a query, CONTINUE Handler, EXIT handler, Loops: Simple, Repeat, While Cursor
- Triggers - Before | After DML Statement
MLOps
- What is MLOps, Different stages in MLOps, ML project lifecycle, Job Roles in MLOps
- What is Development stage of an ML workflow , Pipelines and steps, Artifacts, Materializers, Parameters & Settings
- Stacks & components, Orchestrators, Artifact stores, Flavors etc.
- ML Server infrastructure, Server deployment , Metadata tracking
- Collaborations, Dashboards
ChatGPT
- History and Development of ChatGPT,Examples of ChatGPT use in various industries, Basics of Transformers, Key concepts and principles of Generative AI,Examples of Generative AI models including ChatGPT, open source LLM's, Prompting basics, Overview of Different ChatGPT models
- Prompt Techniques, Few-shot Prompting, Zero Shot prompting, One-Shot Prompting, Chain of Thought Prompting ChatGPT applications in everyday life such as writing,translation and creativity, Explore ChatGPT potential for Education , Work, and Business Use Cases
- Code generation, code explaination, machine translation, structured and unstructred outputs, Canvas, deep research, image generation , video generation, codex, plugins, Browsing
- Utilizing ChatGPT for Excel, word, powerpoint, web development,data anlaysis, programing, Dashboards ,ChatGPTprojects etc.
- Seeking jobs,career changes, working on resume, and updation, networking, job search strategies using ChatGPT, Linkedin Profile Optimization
- Introduction to OpenAI API & usage limits
- Authentication, Endpoint usage
- Integrating GPT with Python, Google Sheets, Excel, Power BI Zapier, Make, LangChain basics
Artificial Intelligence (AI)
- 1. Linear Algebra: Vectors, matrices, dot product, matrix multiplication
- 2. Calculus: Derivatives, partial derivatives, chain rule (for backpropagation)
- 3. Review on ANN
- 1. What is Gradient Descent in detail? Connectivity of Calculus in Back propagation. Weight & Bias Updates,
- 2. Types of Loss functions(MSE, Binary Cross entropy (Binary & Muliti)
- 3. Overfitting Solutions(Dropout, Early stopping)
- 4.Types of Optimizers and acitvations functions its applications
- 5. Example Case study
- 1. CNN,DeepConvolutionModel,DetectionAlgorithm, CNN FaceRecognition
- 2. Working on MNIST data set
- 1. Introduction to Web Scraping & Web Basics,
- 2. Python Libraries for Web Scraping (requests, BeautifulSoup)
- 3. HTML & Web Page Structure Basics
- Selecting a website and extract the data
- 1. To Extract Image, Reviews, Ratings, and Price Tags
- 2. Store in Structured Format
- 3.
- a) Image classfication from images
- b) Sentiment analysis from Reviews
- c) Regression model from Prices.
Big Data
- What is big data, characteristics of big data, technologies in big data etc.
- what is spark environment, spark documentation, installation of spark , spark concepts
- Integration with different languages like python , r, scala, etc. Introducing pyspark environment , pyspark basics and functions
- Pyspark RDD structures, dataframe modules, sql modules , examples , exercise problems, working on datasets
- Pyspak ML libraries, Regression models, linear and logistic regression and clustering basics, tree based models, ensemble concepts
- Pyspark ML applications, with excercises, visualizations
- What is databricks, account creation, cluster creation, working on pyspark applications in databricks with r, python and scala
- What is aws cloud, account creation , understanding basic aws enevironment and knowledge
- What is hadoop , hadoop architecture, creating hadoop environment on AWS cloud, install java, install hadoop and related concepts
- Running applications like map reduce on data , getting insights , doing analysis, word count problems etc.
Azure
- What is cloud computing, why it is important, cloud services, applications, benefits , architectures
- What is Azure, Why Azure, Azure services, Azure core architecture, core azure services domains, creation of azure account
- Intro to AI/ML services, What is azure ml designer studio, developing ml models, python and r applications in studio
- Resource groups, virtual machine concepts , storage service, web apps, databricks environment , azure sql databases, billing etc.
- What is azure open ai, open ai documentation, how to use azure open ai studio, creating applications, different models in azure open ai
Basic Of R
- Data types(Numeric, Char, logical, Complex, Vector, List, Matrix, Factor, Array, Data frame), Relational Operators, Logical Operators
- If, Ifesle, For loop, While loop, Repeat, Functions
- Merging data frames, Analyzing Iris Dataset using apply functions, dplyr package(Filter, Set, Arrange), Data Visuzlization using ggplot2, Scatterplot, Histogram, Boxplot
Popular Job Guaranteed Courses
Our Data Science Course is designed for students, working professionals, and beginners who want to build practical skills in data analysis, statistics, Python, machine learning, and predictive modeling. The course focuses on transforming raw data into meaningful insights that support smarter business decisions. Learners gain hands-on experience with real-world datasets, analytical tools, and industry-focused projects. Whether you are starting after college, planning a career switch, or upgrading your professional skills, this training helps you develop a strong foundation in Data Science. Build job-ready skills, earn certification, and prepare for growing opportunities in data-driven industries.
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❓ Ask
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Learn Best Python Programming for Data Science Course in Navi Mumbai, Thane & Mumbai - Best data scientist professional certificate Training Institute Placement & Online Classes Near Me
Build Your Career with the Best Data Science Course and Practical Training
SofTech Education offers a career-focused Data Science Course designed for students, working professionals, business owners, entrepreneurs and beginners who want to build practical skills in Data Science, AI, Machine Learning and Analytics.
With businesses increasingly depending on data for decision-making, Data Science has become one of the most important technology skills across industries such as IT, banking, finance, healthcare, retail, marketing, manufacturing and e-commerce.
If you are searching for the best Data Science course, a professional Data Science training institute, practical certification training, or a full course for beginners, SofTech Education provides a structured learning program focused on both concepts and real-world applications.
What is Data Science?
Data Science is the process of collecting, organizing, analyzing and interpreting data to identify useful information and support better business decisions.
Companies use Data Science for:
- Customer analysis
- Sales forecasting
- Fraud detection
- Business intelligence
- Customer segmentation
- Marketing analysis
- Product recommendations
- Risk analysis
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- Artificial Intelligence and Machine Learning
Because of its wide application, Data Science skills are becoming relevant not only for technology professionals but also for professionals working in finance, marketing, operations, sales and business management.
Who Can Join the Data Science Course?
The SofTech Education Data Science training course is suitable for:
- College students
- Fresh graduates
- Engineering students
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- Software developers
- Working professionals
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- Finance professionals
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- Entrepreneurs
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- Complete beginners interested in Data Science
You do not necessarily need to come from a programming background to start learning Data Science. Beginners can learn step-by-step with the right guidance from an experienced Data Science trainer.
What Will You Learn in the Full Data Science Course?
The full Data Science course at SofTech Education is designed to build knowledge from fundamentals to practical implementation.
Key topics include:
Python Programming
Learn Python fundamentals, functions, data structures and important libraries used for Data Science.
SQL and Databases
Understand how to retrieve, filter, organize and analyze information stored inside databases.
Statistics for Data Science
Learn probability, correlation, distributions, averages, variance and other statistical concepts required for Data Analysis.
Data Analysis
Work with real datasets, clean information, identify patterns and generate meaningful insights.
Data Visualization
Learn to represent data through charts, reports and dashboards so that business information can be understood easily.
Machine Learning
Understand Machine Learning concepts and how predictive models can be used for forecasting, classification and business decision-making.
Artificial Intelligence
Learn how Data Science, Machine Learning and AI are connected and how modern businesses are using these technologies.
Practical Data Science Training
Learning Data Science only through theory is not enough.
At SofTech Education, the training approach focuses on practical learning and projects.
Students can work on examples such as:
- Sales Data Analysis
- Customer Segmentation
- Marketing Campaign Analysis
- Sales Forecasting
- Customer Churn Prediction
- Product Recommendation
- Business Performance Analysis
Practical projects help learners understand how Data Science is actually used in companies.
Data Science Course for Beginners
Many learners search for a Data Science course for beginners but are concerned about programming or mathematics.
A structured learning approach can make the subject easier.
Beginners can progress through:
Python → SQL → Statistics → Data Analysis → Visualization → Machine Learning → Projects
This step-by-step approach helps students build confidence without trying to learn everything at once.
Data Science Certification
After completing the required course modules and assessments, learners can receive a Data Science course certification from SofTech Education.
Certification can support your professional profile, but SofTech Education also focuses strongly on practical knowledge, projects and skills that learners can demonstrate during interviews.
Career Opportunities After Data Science Training
After developing the necessary skills, learners can explore career opportunities such as:
- Data Analyst
- Data Scientist
- Business Intelligence Analyst
- Machine Learning Engineer
- Business Analyst
- AI/Data Analyst
- Marketing Analyst
- Financial Data Analyst
- Analytics Consultant
Career opportunities will depend on educational background, experience, technical skills and project knowledge.
Data Science Course for Working Professionals
Working professionals can use Data Science training to upgrade their skills or move towards analytics, AI and technology-based roles.
Professionals from finance, sales, marketing, IT and operations can combine their existing industry knowledge with Data Science skills.
This can help them understand data more effectively and make better business decisions.
Data Science for Business Owners and Entrepreneurs
Business owners can also benefit from Data Science.
Understanding analytics can help answer important questions such as:
- Which products are most profitable?
- Which customers generate maximum revenue?
- Which marketing channels perform better?
- Why are customers leaving?
- What could future sales look like?
Business owners do not need to become full-time Data Scientists, but understanding data can help them manage businesses more intelligently.
Why Choose SofTech Education for Data Science Training?
If you are comparing top Data Science training institutes, it is important to look beyond only the course certificate.
SofTech Education focuses on:
- Beginner-friendly learning
- Practical Data Science training
- Structured full course curriculum
- Experienced trainer guidance
- Real-world examples
- Projects and assignments
- Career-focused learning
- Data Science certification
- Training for students and professionals
Data Science Course Fees
The Data Science course fees may depend on the selected batch, training mode, course duration and program structure.
Students looking for information about Data Science course fees, duration, syllabus, certification and upcoming batches can contact SofTech Education directly for the latest details.
Start Your Data Science Journey with SofTech Education
Whether you are a student searching for the right course after college, a working professional planning career growth, a beginner entering technology, or a business owner interested in AI and analytics, Data Science can be a valuable skill for the future.
SofTech Education Data Science Course provides practical, structured and career-oriented training to help learners understand Data Analysis, Python, SQL, Machine Learning and Artificial Intelligence.
SofTech Education – Data Science Training Institute
Courses | Training | Certification | Beginners | Full Course | Practical Learning
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Contact SofTech Education for the latest Data Science course fees, batch timings, curriculum, trainer details and certification information.

