Set yourself on the path to becoming a successful Data Scientist

Learn Python from scratch with topics such as:

• Installing Anaconda & using Jupyter Notebook

• Python syntax, data types, lists, tuples, and dictionaries

• String handling, loops, and decision control

• Functions, modules, and regular expressions

• File I/O, exception handling, and iteration

• Data analysis with NumPy, Pandas, Matplotlib, and Seaborn

• GitHub basics for version control and project management

Outcome: Become confident in writing Python code for data analytics and automation.

Gain mathematical and statistical confidence with:

• Probability & combinatorics

• Linear algebra & differentiation

• Descriptive and inferential statistics

Outcome: Understand the math behind every model and make sense of data patterns.

Build intelligent models from scratch using Scikit-Learn. Topics include:

• Data preprocessing & feature scaling

• Supervised, unsupervised, and reinforcement learning

• Linear & logistic regression

• Decision Tree, Random Forest, KNN, and Naive Bayes models

• Hyperparameter tuning and model selection

• Model evaluation: accuracy, precision, recall, F1 score, ROC, and AUC

• Capstone Project discussion and execution

Outcome: Learn how to pick, build, and fine-tune the right ML model for every problem.

Learn how data is stored, retrieved, and analyzed in databases:

• RDBMS concepts & table operations

• SELECT, WHERE, ORDER BY, JOINs (Inner, Outer, Left, Right)

• Data aggregation, ranking, and top-N analysis

• Advanced SQL queries for data analytics

Outcome: Master SQL for data cleaning, querying, and reporting in real-time business scenarios.

Turn raw data into meaningful insights through interactive dashboards:

• Installing Power BI and connecting to datasets

• Working with filters, slicers, and visual reports

• Building customer segmentation dashboards

• DAX formulas and calculated columns

Outcome: Create dashboards that tell powerful data stories for business stakeholders.

Master AI fundamentals through TensorFlow and Neural Networks:

• Basics of Deep Learning and Neural Network architecture

• Building models in TensorFlow

• Backpropagation and multi-layer networks

• CNN (Convolutional Neural Networks) for image recognition

• NLP (Natural Language Processing) for text analysis and sentiment detection

Outcome: Gain hands-on experience in designing and training neural network models.

• RNN (Recurrent Neural Networks), LSTM, and GRU

• Restricted Boltzmann Machines (RBM) and Autoencoders

• Dimensionality reduction and collaborative filtering

Outcome: Build AI systems capable of sequential learning and predictive intelligence.

• Stay ahead in the AI revolution with modern generative models:

  • Understanding BERT, ChatGPT, and Retrieval-Augmented Generation (RAG)

  • Building Chatbots and Q&A systems using LangChain

  • Creating autonomous AI Agents with Phidata

  • Multi-agent collaboration for real-world AI automation

  Outcome: Learn how modern AI systems like ChatGPT work and build your own smart AI applications.