Course Overview
Cou
rse Overview
Data Science is often hailed as the most exciting and highest-paying career path of the 21st century. The Data Science Course is designed to transform ambitious professionals and students into elite data experts who don’t just analyze the past, but predict the future. Through this comprehensive curriculum, you will master advanced mathematics, complex programming, predictive modeling, and cutting-edge artificial intelligence. Technoglobe delivers this highly intensive, industry-aligned training, taking you from data visualization to deploying live Machine Learning algorithms. Our heavily practical-focused methodology ensures you work on real enterprise challenges, creating a powerful portfolio ready for global tech companies.
Data Science Course Modules
Comprehensive Curriculum Overview
01
Introduction to Data Science & Analytics Lifecycles
02
Advanced Mathematics, Statistics & Probability for DS
03
Python Programming Foundations for Data Science
04
Data Manipulation & Wrangling (NumPy & Pandas)
05
Exploratory Data Analysis (EDA) & Data Visualization
06
SQL & NoSQL Database Systems for Heavy Data
07
Machine Learning: Supervised Learning Core
08
Machine Learning: Unsupervised Learning & Clustering
09
Advanced Machine Learning & Ensemble Tech
10
Introduction to Deep Learning & Neural Networks
11
Text Mining & Natural Language Processing (NLP)
12
Model Deployment, MLOps & Capstone Portfolio
Syllabus
Detailed Topic-wise Curriculum
Module 1: Introduction to Data Science & Analytics Lifecycles
- What is Data Science? Core components and industry use-cases
- Understanding Data Science Pipelines (CRISP-DM framework)
- Types of Data: Structured, Unstructured, and Semi-structured data
- Big Data Concepts vs Traditional Data Frameworks
🛠️ Case Study: Analysis of Netflix/Spotify recommendation pipeline architectures.
Module 2: Advanced Mathematics, Statistics & Probability for DS
- Linear Algebra Basics: Matrices, Vectors, Eigenvalues & Eigenvectors
- Descriptive Stats vs Inferential Statistical Frameworks
- Probability Distributions (Normal, Binomial, Poisson)
- Hypothesis Testing: Z-test, T-test, ANOVA, and Chi-Square Testing
- Understanding P-Value, Type I & Type II Errors
🛠️ Practical: Run A/B testing on multi-version landing page data using Python stats packages.
Module 3: Python Programming Foundations for Data Science
- Setting up Environments: Anaconda Distribution & Jupyter Notebooks
- Python Data Structures: Lists, Tuples, Sets, and Complex Dictionaries
- Control Flow Syntax: Loops, Nested Iterations, and Conditional Statements
- Functions, Modules, List Comprehensions, and Lambda Expressions
- Error Handling & File Input/Output automation in Python
📝 Practical: Write a script to automate log file processing and error categorization.
Module 4: Data Manipulation & Wrangling (NumPy & Pandas)
- NumPy Library: N-dimensional Arrays, Vectorization, and Broadcasting
- Pandas Architecture: DataFrames & Series operations
- Data Cleaning: Handling Nulls, Imputation, Duplicates, and Outliers
- Wrangling: Merging, Joining, Concatenating, and Pivoting Datasets
- Advanced string manipulations and Date-Time indexing tricks
📝 Practical: Preprocess an unformatted raw dataset containing global aviation records.
Module 5: Exploratory Data Analysis (EDA) & Data Visualization
- The Art of Data Exploration: Univariate, Bivariate, and Multivariate analysis
- Static Visualizations with Matplotlib (Line, Bar, Pie, Scatter Plots)
- Advanced Statistical Plots with Seaborn (Histograms, Box plots, Violin plots)
- Correlation Matrices, Heatmaps, and Pairplots to map data relations
- Feature Engineering: Creating new predictive attributes from existing metrics
🛠️ Practical: Perform deep EDA on telecom churn data to visually map major attrition drivers.
Module 6: SQL & NoSQL Database Systems for Heavy Data
- Relational Database Fundamentals for Data Extraction
- Advanced SQL: Multi-table Joins, Subqueries, Grouping, and CTEs
- Connecting Python scripts directly to SQL Servers via DB Connectors
- Introduction to NoSQL Databases & Document Storage (MongoDB)
📝 Practical: Write a Python program that extracts transactional records from SQL and pushes cleaned data to MongoDB.
Module 7: Machine Learning: Supervised Learning Core
- Overview of Machine Learning: Training vs Validation vs Test sets
- Linear Regression for continuous valuation (Cost functions, Gradient Descent)
- Logistic Regression for binary classification systems
- Decision Trees (Entropy, Gini Impurity) & K-Nearest Neighbors (KNN)
- Evaluation Metrics: MSE, R-squared, Confusion Matrix, Precision, Recall, F1-Score, ROC-AUC
📝 Practical: Build a predictive machine learning model to detect credit card fraud risks using Scikit-Learn.
Module 8: Machine Learning: Unsupervised Learning & Clustering
- Introduction to Unsupervised Architectures: Working with unlabelled records
- K-Means Clustering: Choosing K value using the Elbow Method
- Hierarchical Clustering & Dendrogram interpretation
- Dimensionality Reduction: Principal Component Analysis (PCA) mechanics
📝 Practical: Cluster E-commerce shoppers into behavioral groups for hyper-targeted marketing.
Module 9: Advanced Machine Learning & Ensemble Tech
- Understanding Bias-Variance Tradeoff (Overfitting vs Underfitting)
- Ensemble Methods Theory: Bagging vs Boosting concepts
- Random Forest Classifiers and Regressors
- Boosting Algorithms: AdaBoost, Gradient Boosting, and XGBoost
- Hyperparameter Tuning techniques: GridSearchCV and RandomizedSearchCV
📝 Practical: Maximize the accuracy of an insurance claim prediction engine using automated XGBoost parameters.
Module 10: Introduction to Deep Learning & Neural Networks
- Introduction to Artificial Intelligence & Biological vs Artificial Neurons
- Perceptron Architecture & Multi-layer Perceptrons (MLP)
- Understanding Activation Functions (ReLU, Sigmoid, Softmax)
- The mechanics of Forward Propagation and Backpropagation
- Setting up Deep Learning libraries: Tensor Flow & Keras basics
Module 11: Text Mining & Natural Language Processing (NLP)
- Working with Unstructured Text Data in Python
- Text Processing: Tokenization, Stopword removal, Stemming, and Lemmatization
- Converting Text to Numbers: Bag of Words (BoW) & TF-IDF Vectorizers
- Building Sentiment Analysis Engines
📝 Practical: Extract live Twitter/X data feeds and evaluate customer sentiment score trends.
Module 12: Model Deployment, MLOps & Capstone Portfolio
- Saving Models: Serializing model architectures via Pickle / Joblib
- Building Web User Interfaces using the Streamlit / Flask Python frameworks
- Introduction to MLOps: Continuous tracking and basic cloud deployment concepts
- GitHub Repository curation for presenting Data Science project portfolios
🚀 Milestone Target: Presenting an End-to-End Live Predicted Web App + Interview preparation & Placement Track.
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What you’ll learn
- Become a UX designer.
- You will be able to add UX designer to your CV
- Become a UI designer.
- Build & test a full website design.
- Create your first UX brief & persona.
- How to use premade UI kits.
- Create quick wireframes.
- Downloadable exercise files
- Build a UX project from beginning to end.
- Learn to design websites & mobile phone apps.
- All the techniques used by UX professionals
- You will be able to talk correctly with other UX design.
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admin
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