Professional Certificate Program in
Data Science & AI
Empower your future with Python and next-gen technologies like Deep Learning, Generative AI, and NLP
Course Details:
Course Duration :
10-12 Months (Depending on Intensity)
Classes :
2 or 3 days a week
Mode:
Classroom / Online / Hybrid
Prerequisites :
Basic knowledge of mathematics, statistics, and programming fundamentals.
Payment Mode:
Advanced and installment basis
Job Assistance:
100%
Fees:
โน 45,000/- ( Starting from)
Course Description:
Step confidently into the world of tomorrow with a transformative Data Science and AI Course Kolkata โ a perfect blend of foundational knowledge and cutting-edge innovation. Whether you’re beginning your journey or looking to upskill, this course takes you from basic to advanced levels of Data Science, Data Analytics, and AI.
Offered by the renowned Ascent Infotech Computer Training Institute in Kolkata, this hands-on, industry-aligned course equips you with practical expertise in Python programming, data analysis, machine learning, and AI-driven tools, along with real-time project exposure and case-based learning.
Explore a well-structured curriculum that not only covers core programming with Python but also dives into advanced domains like Deep Learning, Generative AI, and Natural Language Processing (NLP) โ ensuring you’re prepared to tackle real-world challenges in the evolving tech landscape.
Whether youโre searching for a Data Science Course in Kolkata, a Data Analytics Course in Kolkata, or an all-in-one Artificial Intelligence in Data Science Course in Kolkata, this comprehensive program has it all.
Recognized as the Best Data Science Institute in Kolkata, Ascent Infotech delivers top-tier education, expert faculty, and placement support to help you succeed. So, if you’re Googling for the right Data Science & AI course near me, look no further โ your future starts here!
Course Heights:
Unlock 50+ Essential Cutting-Edge Industry Tools
Anaconda
VS Code
Google Colab
Jupytor
Python
Numpy
Pandas
Matplotlib
Scikit-learn
XGBoost
LightGBM
Seaborn
MYSQL
Git
Github
Docker
BeautifulSoup
Scrapy
TensorFlow
Keras
PyTorch
Hadoop
Spark
Tableau
Power BI
Flask
Airflow
Kubeflow
Kaggle
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Module 1: Introduction to Data Science
- What is Data Science?
- Data Science Life Cycle
- Roles and Responsibilities of a Data Scientist
- Tools & Technologies used in Data Science
- Applications of Data Science across industries
Module 2: Mathematics & Statistics for Data Science
Statistics
- Descriptive & Inferential Statistics
- Probability & Probability Distributions
- Hypothesis Testing & Confidence Intervals
- Central Tendency & Dispersion
Linear Algebra:
- Matrices, Vectors
- Eigenvalues and Eigenvectors
Calculus :
- Matrices, Vectors
- Eigenvalues and Eigenvectors
Module 3: Python for Data Science
- Python Basics: Variables, Data Types, Control Structures
- Functions, Modules, File I/O
- OOPs Concepts in Python
- Working with Libraries:
- NumPy: Arrays, Broadcasting, Vectorization
- Pandas: DataFrames, Data Cleaning, GroupBy
- Matplotlib & Seaborn: Data Visualization
Module 4: Data Wrangling & Data Preprocessing
- Data Cleaning & Handling Missing Values
- Data Transformation, Feature Engineering
- Encoding Categorical Variables
- Outlier Detection & Treatment
- Feature Scaling: Normalization & Standardization
Module 5: Exploratory Data Analysis (EDA)
- Understanding Data Distributions
- Univariate, Bivariate & Multivariate Analysis
- Correlation & Covariance
- Visualizations: Histograms, Boxplots, Heatmaps, Pair Plots
Module 6: Machine Learning Algorithms
Supervised Learning:
- Linear & Logistic Regression
- Decision Trees, Random Forest
- Support Vector Machine (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes Classifier
Unsupervised Learning:
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA)
Model Evaluation:
- Confusion Matrix, Accuracy, Precision, Recall, F1-Score
- Cross-Validation, ROC-AUC, Grid Search
Module 7: Advanced Machine Learning & Deep Learning
Ensemble Learning:
- Bagging, Boosting (XGBoost, LightGBM)
Introduction to Neural Networks:
- Perceptron, Activation Functions
- Backpropagation, Gradient Descent
Deep Learning with TensorFlow/Keras:
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
Transfer Learning and Fine-Tuning
Module 8: Natural Language Processing (NLP)
- Text Preprocessing (Tokenization, Lemmatization)
- Bag of Words, TF-IDF
- Sentiment Analysis, Topic Modeling
- Word Embeddings (Word2Vec, GloVe)
- Text Classification with NLP Models
Module 9: Time Series Analysis
- Time Series Components (Trend, Seasonality)
- Moving Averages & Exponential Smoothing
- ARIMA, SARIMA models
- Forecasting Techniques
Module 10: SQL for Data Science
- SQL Basics: SELECT, WHERE, JOINs, GROUP BY
- Subqueries & Window Functions
- Data Manipulation and Aggregation
- Using SQL with Python (SQLite, MySQL)
Module 11: Big Data Fundamentals
- Introduction to Big Data & Hadoop Ecosystem
- Basics of HDFS, Hive, Pig, MapReduce
- Introduction to Apache Spark for Data Processing
- Using PySpark for Data Science Projects
Module 12: Data Visualization & BI Tools
- Data Storytelling Techniques
- Dashboards with:
- Power BI
- Tableau
- Python Dash / Streamlit
Module 13: Creation of API using Flask
- Model Deployment Essentials
- Serving Machine Learning Models via API Endpoints
- Integration with Front-End Interfaces:
- Web Applications
- Mobile Apps
- Visualization Dashboards
Module 14: Capstone Projects (Live Projects)
Students will work on 2-3 end-to-end projects with real-world datasets:
- E.g., Predictive Modeling (Sales / House Price)
- Fraud Detection System
- Sentiment Analysis on Tweets
- Customer Segmentation
- Time Series Forecasting (Stock/Weather)