Full Stack Data Science

Full Stack Data Science

Master the complete data science lifecycle — from data wrangling and visualization to machine learning, deep learning, NLP, and computer vision — with hands-on projects.

Course Overview:

  • Advanced Excel
  • PowerBI
  • Tableau
  • SQL
  • Python
  • Statistics
  • Machine Learning
  • Big Data
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision

Category: Tags: , , , , , ,

Advanced Python

  • Introduction to Python

  • Exploring Anaconda and other IDEs

  • Identifiers, Comments, Keywords

  • Datatypes & Data Structures

    • Strings and Operations

    • Lists and Operations

    • Tuples and Operations

    • Sets and Operations

    • Dictionaries and Operations

  • Operators & Control Flow

    • Types of Operators

    • Binary Numbers

    • Decision Control Statements, Nested If

    • Loop Statements & Nested Loops

    • Jump Control Statements

  • Functions in Python

    • Types, Arguments, Built-in Functions

    • Recursion, Lambda Functions

  • Modules & Exception Handling

    • Types, Creating & Importing Modules

    • Errors vs Exceptions, Raising & Handling Exceptions

    • Nested Try Blocks, Exception Types

  • File Handling

    • File Types, File Modes, Paths

    • File Handling Methods

  • OS Module: Functions & Methods

  • OOPs Concepts

    • Classes & Objects, Methods

    • Principles: Inheritance, Encapsulation, Polymorphism, Abstraction

    • Getters, Setters, Deleters

  • Multithreading

  • Libraries

    • NumPy (Arrays, Dimensions, Functions & Methods)

    • Pandas (Series, DataFrames, CSV/JSON Handling, Cleaning Data)

    • Tkinter (GUI Development)

    • Matplotlib (Plotting, Graphs, Charts)

  • Multiple Python-Based Projects

SQL

  • Database Basics: Understanding Databases, RDBMS vs NoSQL

  • SQL Syntax & Environment Setup

  • Database Operations: Create, View, Drop, Use

  • Tables: Creating, Viewing, Modifying

  • Datatypes in SQL

  • CRUD Operations (Insert, Retrieve, Update, Delete)

  • Functions in SQL

  • Joins, Grouping & Filtering, Subqueries, Set Operations

  • Constraints, Procedures in SQL

Statistics

  • Introduction, Importance & Applications

  • Types of Statistics: Qualitative vs Quantitative

  • Data Collection, Sampling & Techniques

  • Data Representation: Tables, Graphs, Charts

  • Measures of Central Tendency: Mean, Median, Mode

  • Dispersion: Variance, Standard Deviation

  • Skewness & Kurtosis

  • Correlation & Regression

  • Probability & Hypothesis Testing

    • Null vs Alternate Hypothesis

    • Type I & II Errors

    • Z-test, T-test, Chi-Square, ANOVA, F-test

    • Simple Linear Regression

Advanced Excel

  • Introduction, Importance & Interface

  • Basic Excel Operations & Formatting

  • Data Transformation, Formulas & Functions

    • Logical, Math, Lookup Functions

  • Data Validation, Sorting & Filtering

  • Pivot Tables, Pivot Charts, Slicers & Timeline

  • Conditional Formatting, Flash Fill, Consolidation

  • Charts & Graphs

  • Macros & Worksheet Protection

Power BI

  • Power BI Desktop vs Service

  • Reports, Dashboards, Datasets

  • Data Sources: Import vs DirectQuery

  • Power Query Editor: Cleaning & Transforming Data

  • Data Models & Relationships

  • DAX (Data Analysis Expressions)

    • Calculated Columns, Measures

    • Variables, Functions (Logical, Math, Lookup, Time Intelligence)

  • Visualizations: Charts, KPIs, Maps, Funnel, Treemaps, Customization

  • Filters, Slicers, Drill Downs, Bookmarks

  • Dashboards: Design, Interactivity, Publishing

  • Scheduled Refresh, Security (RLS), Roles

  • Q&A Visuals, Smart Narratives, Key Influencers, Decomposition Tree

  • Performance Optimization & Best Practices

Machine Learning

  • Introduction: AI vs ML vs DL vs Data Science

  • ML Types: Supervised, Unsupervised, Reinforcement

  • ML Setup: Anaconda, Jupyter, Libraries (NumPy, Pandas, Matplotlib, Seaborn)

  • Data Preprocessing: Missing Values, Encoding, Feature Scaling, Engineering

  • EDA (Exploratory Data Analysis): Stats, Visualization, Correlation, Outliers

  • Supervised Learning

    • Regression: Linear, Multiple, Polynomial

    • Classification: Logistic Regression, KNN, SVM, Decision Trees, Random Forest, Naive Bayes

  • Model Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1, ROC-AUC, RMSE, MAE

  • Unsupervised Learning

    • Clustering: K-Means, Hierarchical, DBSCAN

    • Dimensionality Reduction: PCA

  • Feature Engineering: Importance, Selection, Multicollinearity Removal

  • Model Deployment Basics: Pickle/Joblib, Streamlit/Flask, Cloud Overview

  • Advanced Topics

    • Ensemble Learning: Bagging, Boosting (AdaBoost, XGBoost)

    • Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV)

    • Time Series Forecasting

    • NLP Basics

    • ML Ethics & Bias

  • Real-World Project Workflow

Big Data

  • Big Data Concepts: 5Vs, Architecture, Batch vs Stream

  • Hadoop Ecosystem: HDFS, YARN, Hive, Pig, HBase

  • Data Transfer: Sqoop, Flume

  • Apache Spark: RDDs, DataFrames, Spark SQL, PySpark, Spark Streaming

  • Big Data on Cloud: AWS, Azure, GCP

  • Big Data Use Cases

Cloud Computing

  • Basics: Service Models (IaaS, PaaS, SaaS), Deployment Models (Public, Private, Hybrid)

  • Virtualization & Hypervisors

  • AWS, Azure, GCP Overview

  • Cloud Accounts & Services: EC2, S3, IAM, Networking, Billing

  • Serverless Basics: AWS Lambda

  • Cloud Security & Monitoring

  • Deploying Web Apps on Cloud

Tableau

  • Tableau Interface & Setup

  • Connecting to Data Sources (Excel, CSV, Databases)

  • Data Cleaning & Preparation

  • Dimensions, Measures, Aggregations

  • Charts: Bar, Line, Pie, Scatter, Maps, Dual Axis, Combo, Treemaps, Bubble

  • Calculated Fields, Parameters, Sets, Groups, LOD Expressions

  • Dashboards: Interactivity, Actions, Filters, Story Points

  • Publishing Dashboards (Public/Server)

  • Exporting Reports

  • Best Practices & Real-World Projects

Deep Learning

  • Introduction: AI vs ML vs DL, Applications, History

  • Math Foundations: Linear Algebra, Calculus, Probability, Optimization

  • Python & DL Libraries: TensorFlow, PyTorch, Keras, NumPy, Pandas, Matplotlib

  • ANNs: Perceptrons, Activation Functions, Feedforward, Backpropagation, Loss Functions, Regularization

  • CNNs: Convolution, Pooling, Transfer Learning (VGG, ResNet, Inception), Projects in Image Classification

  • RNNs: Architecture, LSTM, GRU, Applications (Text, Time Series, Sentiment)

Natural Language Processing (NLP)

  • Basics: Text Data, Tokens, Corpus, Preprocessing

  • Feature Extraction: Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe, FastText)

  • Text Classification: Sentiment, Spam, News Topic, NER, POS Tagging

  • Sequence Models: RNNs, LSTM, GRU, Attention, Seq2Seq

  • Advanced NLP: Summarization, Translation, QA, Zero-shot Learning

  • Model Deployment (Streamlit, Flask, Cloud)

  • Ethics in NLP

Computer Vision

  • Basics: Images, Pixels, Channels, Formats, OpenCV

  • Image Processing: Thresholding, Blurring, Edge Detection, Morphology

  • Deep Learning for CV: CNNs, Transfer Learning, Data Augmentation

  • Object Detection: R-CNN, Fast/Faster R-CNN, YOLO, SSD

  • Image Segmentation: Semantic, Instance, U-Net, Mask R-CNN

  • Applications: Face/Gesture Recognition, OCR, Video Processing & Object Tracking

  • Deployment on Web, Cloud, or Mobile

Course Outcomes

  • Gain strong Python & SQL skills for data handling.

  • Apply statistics & probability in data analysis.

  • Perform data visualization with Excel, Power BI, and Tableau.

  • Build and evaluate Machine Learning models.

  • Work with Big Data tools like Hadoop & Spark.

  • Deploy solutions on cloud platforms (AWS, Azure, GCP).

  • Implement Deep Learning, NLP, and Computer Vision applications.

  • Integrate AI, Big Data, and Cloud for real-world projects.

  • Develop and present capstone projects simulating industry workflows.

Related Courses

Join Our Community

Enter your email address to register to our newsletter subscription delivered on regular basis!