Course Highlights
🧠 In-Depth Curriculum
💻 100% Practical Training
📊 Weekly Learning Plan
🧩 Capstone Project
👨🏫 Mentor Support
🎓 Career Assistance
Data Science (AI/ML)
Duration 3 Months
WEEK 1
Introduction to Data Science
- What is data science and its importance
- Data science life cycle
- Key skills required for data science
- Real-world applications of data science
WEEK 2
Python for data science
- Basics of python programming
- Data types, lists, tuples, sets, dictionaries
- Functions, loops, and conditions
- File handling and regular expressions
WEEK 3
Data manipulation with pandas and numpy
- Introduction to numpy arrays
- Dataframes and series in pandas
- Data cleaning, merging, and filtering
- Handling missing and duplicate data
WEEK 4
Data visualization
- Introduction to matplotlib and seaborn
- Plot types – line, bar, histogram, scatter, pie
- Advanced visualization with seaborn
- Storytelling with data visualization
WEEK 5
Statistics for data science
- Descriptive and inferential statistics
- Probability distributions
- Hypothesis testing
- Correlation and regression
WEEK 6
Exploratory data analysis (EDA)
- Data profiling and data summary
- Outlier detection and treatment
- Feature engineering basics
- EDA using pandas and matplotlib
WEEK 7
Machine learning introduction
- Supervised vs unsupervised learning
- Linear and logistic regression
- Model evaluation metrics
- Data splitting and cross-validation
WEEK 8
Classification algorithms
- Decision tree and random forest
- K-nearest neighbors (KNN)
- Naïve bayes classifier
- Model tuning and accuracy improvement
WEEK 9
Unsupervised learning
- K-means clustering
- Hierarchical clustering
- PCA and dimensionality reduction
- Real-world clustering case study
WEEK 10
Time series analysis
- Time series concepts and components
- ARIMA and exponential smoothing
- Forecasting models in python
- Hands-on project on stock prediction
WEEK 11
Introduction to AI and deep learning
- What is artificial intelligence
- Neural networks basics
- Introduction to tensorflow and keras
- Deep learning project basics
WEEK 12
Capstone project & deployment
- Building an end-to-end project
- Model deployment using streamlit
- GitHub version control
- Resume and portfolio preparation
Power Module
- Apache Spark Project
- Top Apache Spark Interview Questions
- Data Structures & Algorithms
Profile Optimisation
- Resume Building
- LinkedIn Optimisation
- Naukri Profile Optimisation
M1 Completed
START GIVING INTERVIEWS
Data Science (AI/ML)
Duration 3 Months
WEEK 1
Introduction to machine learning and linear algebra
- Overview of machine learning and its types
- Machine learning vs traditional programming
- Real-world applications of machine learning
- Overview of the ML workflow
- Linear algebra review: eigen values, vectors, matrix operations
WEEK 2
Python for machine learning
- Review of basic python concepts
- Setting up Jupyter and scikit-learn environment
- Python libraries for ML: numpy, pandas, scikit-learn
- Data preprocessing and feature scaling
- Handling missing values and encoding
WEEK 3
Supervised learning regression
- Linear regression and its applications
- Polynomial regression and model complexity
- Model evaluation: MSE, RMSE, R-squared
- Hands-on regression practice
WEEK 4
Supervised learning classification
- Logistic regression for binary classification
- k-nearest neighbors (kNN)
- Model metrics: accuracy, precision, recall, F1-score
- Building and evaluating classification models
WEEK 5
Decision trees, ensemble methods, and SVM
- Decision trees and tree-based models
- Bagging and boosting concepts
- Random forests and gradient boosting
- Support vector machines and kernel tricks
- Applying ML algorithms and hyperparameter tuning
WEEK 6
Model evaluation and selection
- Cross-validation techniques
- Grid search and random search
- Understanding bias-variance tradeoff
- Comparing model performance
WEEK 7
Unsupervised learning and clustering
- K-means and hierarchical clustering
- PCA and t-SNE for dimensionality reduction
- Visualizing high-dimensional data
- Real-world clustering case study
WEEK 8
Natural language processing (NLP)
- Text preprocessing and feature extraction
- Tokenization, stemming, lemmatization
- Bag-of-words, TF-IDF, and word embeddings
- Implementing NLP models with NLTK and spaCy
WEEK 9
Neural networks and deep learning
- Basics of neural networks
- Neurons, layers, and activation functions
- Backpropagation and gradient descent
- Building neural networks using python
WEEK 10
Introduction to generative AI
- Introduction to generative AI and LLMs
- Prompt engineering techniques
- Transformer architectures
- Understanding RAG and applications
WEEK 11
Building LLM applications
- LangChain workflows for Gen AI
- Fine-tuning and customization of LLMs
- Using AI tools for NLP and business analysis
- Developing AI-powered applications
WEEK 12
Capstone project preparation
- Choosing project topic and dataset
- Forming project teams and proposals
- Project planning and milestones
- Initial data exploration and feedback sessions
WEEK 13-14
Capstone project work and presentation
- Independent and group project work
- Regular mentor check-ins and peer reviews
- Final project presentation and evaluation
- Course wrap-up and feedback
Power Module
- Apache Spark Project
- Top Apache Spark Interview Questions
- Data Structures & Algorithms
Profile Optimisation
- Resume Building
- LinkedIn Optimisation
- Naukri Profile Optimisation
M1 Completed
START GIVING INTERVIEWS
WEEK 1 – 3
- Big Data – The Big Picture
- Distributed Storage & Processing Fundamentals
WEEK 4 - 9
- Apache Spark In-Depth
WEEK 10 - 11
- Apache Spark Optimizations
WEEK 12 - 13
- Apache Spark Optimizations
Course Fee
For Students accessing the course from India
(Duration: 7 Months – Validity: 2 Years)
Data Science (AI/ML) Payment Link with 15% Discount:
Data Analytics using PowerBI Payment Link with 15% Discount:
Azure Data Engineering Payment Link with 15% Discount:
FullStack Python payment Link :
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Our Students Placed In
Our Alumni work at eminent Big data companies and progressive Startups
Students Say’s About Our Institute
Our Students Testimonials
Harikrishna K.
I attended MSBI(SSIS,SSAS),SQL Server ,Power BI ,Data Warehouse Courses by Under Bhaskar Jogi Sir Training. Bhaskar ,for MSBI and power BI learners very good platform Go online Trainings Institute .I learned a lot from Bhaskar sir’s teaching ,Thank You So Much Sir.
Arjun Sudheer
I am really impressed with the way Bhaskar sir teaches each and every concept! He is very passionate about teaching and that can be experienced with the way he teaches each topic, with his teaching anyone can learn any technology with ease! He teaches each and every concept .
Rajsekhar Koneru
I have completed power bi ,SQL server, Data Warehouse and SSIS with Bhaskar Jogi sir. The training is fabulous. I never seen this kind of training in my life. Thanks a lot Bhaskar sir. I recommend this training to all students who wanted a job in IT sector.
Sai Kumar Reddy
Am attending the PBI, DWH, SQL Server by Bhaskar Jogi Sir. I strongly recommend this course and the teaching by Bhaskar Jogi is very easy to understand with real time case study. Even non IT people can learn with the teachings of Bhaskar Jogi and get into the IT Industry.
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