📈 Your Market Value After This Course
What you'll achieve and how much you can earn after completing Data Science
Fresher / Entry Level
₹6 - 10 LPA
0-2 years experience
- Junior Data Scientist
- ML Trainee
⭐ Most Common
Mid Level
₹12 - 22 LPA
2-5 years experience
- Data Scientist
- Machine Learning Engineer
Senior / Expert Level
₹25 - 55+ LPA
5+ years experience
- Lead Data Scientist
- AI Research Scientist
🎯 Job Roles You Can Apply For
Data Scientist
Machine Learning Engineer
Data Analyst
Deep Learning Engineer
NLP Engineer
Computer Vision Engineer
AI Engineer
⚡ Skills You'll Master
Python
Pandas & NumPy
Machine Learning
Deep Learning
Statistics
Data Visualization
SQL
TensorFlow/Keras
NLP
Computer Vision
Big Data (Spark)
MLOps & Deployment
📚 Complete Course Syllabus
Master every aspect with our comprehensive curriculum
Module 1: Python for Data Science
- Python Basics - Variables, Data Types, Operators
- Control Flow - if-else, Loops (for, while)
- Functions & Lambda Expressions
- Data Structures - Lists, Tuples, Dictionaries, Sets
- List Comprehensions & Generators
- File Handling - Reading/Writing CSV, JSON, Excel
- NumPy - Arrays, Mathematical Operations
- Pandas - Series, DataFrame, Data Manipulation
- Matplotlib & Seaborn - Data Visualization
- Object Oriented Programming in Python
- Error Handling & Debugging
Module 2: Mathematics & Statistics
- Linear Algebra - Vectors, Matrices, Eigenvalues
- Matrix Operations - Dot Product, Transpose, Inverse
- Calculus - Derivatives, Partial Derivatives, Gradients
- Descriptive Statistics - Mean, Median, Mode, Variance
- Probability - Distributions, Bayes Theorem
- Inferential Statistics - Hypothesis Testing, p-value
- Correlation & Covariance
- Normal Distribution, Central Limit Theorem
- ANOVA & Chi-Square Tests
- Linear Algebra for Machine Learning
Module 3: Data Manipulation with Pandas
- Introduction to Pandas - Series & DataFrame
- Reading Data - CSV, Excel, JSON, SQL
- Data Inspection - head, tail, info, describe
- Data Cleaning - Handling Missing Values (dropna, fillna)
- Handling Duplicates & Outliers
- Data Transformation - apply, map, replace
- Filtering & Sorting Data
- Grouping & Aggregation - groupby, agg, pivot_table
- Merging, Joining, Concatenating DataFrames
- Time Series Analysis
Module 4: Data Visualization
- Introduction to Data Visualization
- Matplotlib - Line, Bar, Scatter, Histogram Plots
- Customizing Plots - Labels, Titles, Legends, Colors
- Subplots & Multiple Plots
- Seaborn - Statistical Visualizations
- Heatmaps, Pairplots, Boxplots, Violin Plots
- Plotly - Interactive Visualizations
- Power BI & Tableau for Data Science
- Exploratory Data Analysis (EDA)
Module 5: Machine Learning - Supervised Learning
- Introduction to Machine Learning
- Linear Regression - Simple & Multiple
- Polynomial Regression
- Logistic Regression for Classification
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes Classifier
- Gradient Boosting (XGBoost, LightGBM)
- Model Implementation with Scikit-learn
Module 6: Machine Learning - Unsupervised Learning
- Clustering - K-Means Algorithm
- Hierarchical Clustering - Dendrograms
- DBSCAN Clustering
- Principal Component Analysis (PCA)
- t-SNE for Dimensionality Reduction
- Anomaly Detection
- Association Rule Learning - Apriori, FP-Growth
Module 7: Feature Engineering & Model Selection
- What is Feature Engineering
- Feature Scaling - Standardization, Normalization
- Feature Encoding - One-Hot, Label Encoding
- Feature Selection Techniques
- Handling Imbalanced Data
- Model Evaluation Metrics - Accuracy, Precision, Recall, F1
- Cross-Validation - K-Fold, Stratified K-Fold
- Hyperparameter Tuning - GridSearchCV, RandomizedSearchCV
- Bias-Variance Tradeoff
Module 8: Ensemble Learning
- What is Ensemble Learning
- Bagging - Bootstrap Aggregating
- Random Forest - Bagging with Decision Trees
- Boosting - AdaBoost, Gradient Boosting
- XGBoost - Extreme Gradient Boosting
- LightGBM & CatBoost
- Stacking & Voting Classifiers
Module 9: Deep Learning & Neural Networks
- Introduction to Neural Networks
- Perceptron & Activation Functions - ReLU, Sigmoid, Tanh
- Forward Propagation & Backpropagation
- Optimizers - SGD, Adam, RMSprop
- Loss Functions - MSE, Cross-Entropy
- TensorFlow & Keras Basics
- Building Neural Networks with Keras
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- LSTM & GRU Networks
- Transfer Learning & Pre-trained Models
- Autoencoders & Generative Models
Module 10: Natural Language Processing (NLP)
- Introduction to Natural Language Processing
- Text Preprocessing - Tokenization, Stemming, Lemmatization
- Removing Stopwords & Punctuation
- Bag of Words & TF-IDF
- Word Embeddings - Word2Vec, GloVe, FastText
- Sentiment Analysis
- Named Entity Recognition (NER)
- Text Summarization
- Text Classification
- Transformers & BERT
Module 11: Computer Vision (OpenCV)
- Introduction to Computer Vision
- OpenCV Basics - Reading, Writing, Displaying Images
- Image Processing - Filtering, Thresholding, Edge Detection
- Face Detection with Haar Cascades
- Object Detection with YOLO
- Image Classification with CNNs
- Real-time Video Processing
Module 12: Big Data & Cloud for Data Science
- Introduction to Big Data - 5 Vs
- Hadoop Ecosystem - HDFS, MapReduce
- Apache Spark - RDD, DataFrame, Spark SQL
- PySpark for Data Science
- SQL for Data Science
- Cloud Platforms - AWS S3, EC2, SageMaker
- Azure ML & GCP Vertex AI
- MongoDB & NoSQL Databases
Module 13: MLOps & Model Deployment
- What is MLOps - ML Lifecycle Management
- Model Serialization - Pickle, Joblib
- Deploying ML Models with Flask
- Deploying ML Models with FastAPI
- Deploying on Cloud - AWS SageMaker
- MLflow for Model Tracking
- Docker for Model Deployment
- CI/CD for ML Models
- Model Monitoring & Retraining
Module 14: Real-World Projects
- Project 1: House Price Prediction
- Project 2: Customer Churn Prediction
- Project 3: Credit Card Fraud Detection
- Project 4: Image Classification using CNN
- Project 5: Sentiment Analysis on Reviews
- Project 6: Recommendation System
- Project 7: Sales Forecasting
- Capstone Project - End to End Data Science Solution
⭐ Why Choose Tekksol Global?
We provide the best learning experience with industry experts
Expert Trainers
Learn from industry professionals with 12+ years of Data Science experience
Hands-on Projects
Work on 10+ real-time Data Science projects with industry datasets
Industry Certification
Get globally recognized Data Science certification
100% Placement Support
Tie-ups with 400+ companies for Data Science roles
Resume Building
Professional resume & portfolio with Data Science projects
Mock Interviews
Regular mock interviews with detailed feedback
💻 Real-Time Projects
Build impressive portfolio with industry-relevant projects
Customer Churn Prediction
Build a machine learning model to predict customer churn using classification algorithms and provide business insights.
Python
Pandas
Scikit-learn
XGBoost
Matplotlib
Image Classification with CNN
Create a deep learning model using CNN to classify images into multiple categories like animals, objects, and scenes.
Python
TensorFlow
Keras
CNN
OpenCV
End-to-End ML Pipeline Deployment
Build complete ML pipeline from data collection to model deployment using Flask/Streamlit on AWS/GCP.
Python
Scikit-learn
Flask
Docker
AWS
🚀 Placement Assistance
We're committed to your success beyond the course
Placement Support Includes:
- Resume & LinkedIn Profile Building
- Aptitude & Technical Training
- Mock Interviews with Industry Experts
- Soft Skills & Communication Training
Our Hiring Partners:
- 500+ Hiring Partners
- Unlimited Interview Opportunities
- Job Portal Access
- Life-long Placement Support
Our Top Hiring Partners
❓ Frequently Asked Questions
Got questions? We've got answers
What are the prerequisites for Data Science Master program?
Basic programming knowledge is helpful. We cover Python from scratch, so no prior Data Science experience required.
What is the duration of the program?
The program duration is 6 months (220 hours) with flexible batch timings.
Will I learn both ML and Deep Learning?
Yes, the course covers Machine Learning, Deep Learning, NLP, Computer Vision, and Big Data.
What projects will I build?
You will build 10+ projects including Churn Prediction, Image Classification, and End-to-End ML Pipeline.
Is placement assistance provided?
Yes, we provide 100% placement assistance with 400+ hiring partners.
🚀 Ready to Start Your Data Science Journey?
Fill the form below and our counselor will contact you within 24 hours
✅ 100% Secure | ✅ No Spam | ✅ Free Counseling