Course Description
Advanced Certification in Data Science and AI from IITM Pravartak
Excel in your career with an Advanced Certification in Data Science and AI from IITM Pravartak. This data science course is an ideal combination of theory, case studies and capstone projects. Its curriculum has been designed by the university and is considered the best in the country. Post certification, one will gain alumnus status from the university.
What is the Certification Process
Data science course in Bangalore includes live-virtual sessions with IITM professors and an optional two-day campus immersion in Chennai. After completing the training, you will get an Advanced Certification in Data Science and AI from IITM Pravartak.
What is IIT Madras Pravartak Training and Certification
ExcelR, in association with IIT Madras, offers an add-on certification for your data science course in Bangalore.
This includes:
- 15+ hours of live-virtual sessions with IITM professors
- Optional 2-day campus immersion at IITM, Chennai
- Prestigious IITM Pravartak certification
Data Science Cоursе Training
ExcelR offers data scientist cоursе in Bangalore, an extensive and job-ready training. It covers the complete data science lifecycle concepts, including data collection, data extraction, data cleansing, feature engineering, and more. Skills and tools ranging from statistical analysis and machine learning to natural language processing and Tableau, as well as Spark and Hadoop, are covered in the training. Additionally, course participants receive placement assistance in top IT companies.
Why Join this Program with ExcelR
Data Science Certification from IITM Pravartak & ExcelR
- Career-Focused Training From learning to job placement support
- Proven Track Record Thousands placed in top tech companies across India & abroad
- Dedicated Placement Cell 5000+ corporate hiring partners
- University Partnerships Training partner for 1000+ universities (IIM, BITS, IITM, etc.)
- Expert Faculty Trainers with 20+ years’ experience, IIT/IIM/ISB alumni & PhDs
- Blended Learning Model Classroom, live online, and e-learning access in one program
- Unlimited Access - Jumbo Pass Attend multiple online sessions with different trainers for 1 year
- Prestigious Certification Data Science Certification from IITM Pravartak & ExcelR
Why Opt for a Data Science Course
With digitalisation producing massive volumes of data, the demand for skilled data science professionals has surged. Data science roles are among the most sought-after, offering a stable and rewarding career path.
- 1.4 lakh jobs open in Data Science, AI, and Big Data (NASSCOM)
- 2.3 lakh professional shortfall projected globally by 2021
- 417% growth in demand for data science roles in India (Talent Supply Index, 2018)
- #1 job to pursue, as ranked by Glassdoor in 2018
- Harvard Business Review stated that ‘Data Scientist is the sexiest job of the 21st century’.
Who Can Enrol in this Data Science Course
Professionals who can consider data scientist cоursе in Bangalore as the next logical move to enhance their careers include:
- Professionals from any domain with logical, mathematical and analytical skills.
- Professionals working on business intelligence, data warehousing and reporting tools.
- Statisticians, economists, and mathematicians.
- Software programmers, business analysts, and Six Sigma consultants.
- Fresher from any stream with good analytical and logical skills.
Interview Preparation Sessions
As part of this program, aspirants will receive complete interview preparation support.
- Participants who complete this course and projects will be eligible to enter our Placement Incubation Program.
- A detailed repository of data science interview questions with answers will be shared for practice.
- A dedicated Data Science Subject Matter Expert(SME) will guide participants in building strong resumes.
- Mock interviews will be conducted, followed by personalised feedback on performance.
- Continuous support and guidance will be provided until the participant secures a job.
- Additional guidance will be offered on LinkedIn profile building and improving resume visibility.
Projects
"Daily" Twitter Data Analysis for a Product
As more and more people are expressing their views and opinions on various microblogging websites about various products and services. There has been a surge of data generated by the users, these websites have people sharing their thoughts daily.
Sentiment Analysis with the help of Natural Language Processing technique for identifying the sentiments of a product or service
Natural Language Processing
Customers are looking for more information before buying a product on E-commerce websites. Amazon introduced a new feature 'question and answer' search field for products.
The project is to build an information retrieval system from Amazon products data based on NLP techniques. Top 5 relevant answers to be retrieved based on input question
Predicting Loan defaulters
Reducing the risk of fraudulent loans by carefully evaluating the risk & at the same time increasing profits by rejecting only those loans, which have the potential of defaulting
Warranty Cost prediction
The objective of the analysis to predict an item when sold, what is the probability that customer would file for warranty and to understand important factors associated with them
Predict flight delays
Predict which flights would be delayed and by how long?
Flight delays cost the industry an estimated $25 billion every year More than 60 percent of frequent flyers cite delays among the things about air travel that they find most dismaying. And the costs are spread around - an extra $25 in parking here, a missed business meeting there. Carriers, meanwhile, pay an estimated $62 per minute in crew, fuel, maintenance and other costs. It adds up.
Career Progression and Salary Trends
Learning Path
Course Curriculum
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
- 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
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