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Diploma in Data Science & AI

Become an Expert in

Data Science & AI

Comprehensive curriculum including Python & advanced concepts like Deep Learning, Gen AI & NLP

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Course Details:

Course Duration :

12-14 Months (Depending on Intensity)

Classes :

2 or 3 days a week

Mode:

Classroom / Online / Hybrid

Prerequisites :

Basic programming knowledge, high school-level mathematics (algebra, statistics)

Payment Mode:

Advanced and installment basis

Job Assistance:

100%

Fees:

₹ 75000/- (Offer Price of ₹ 55000/-*, Offer limited)

Course Overview :

A well-structured curriculum covering Python along with advanced topics such as Deep Learning, Generative AI, and Natural Language Processing.

Unlock the power of data with the Data Science & AI Course offered by Ascent Infotech Computer Training Institute, a renowned and trusted name in Kolkata. This industry-oriented program is designed for aspiring professionals who want to gain hands-on experience in data analysis, machine learning, Python programming, AI tools, and real-world business intelligence applications.

Whether you’re looking for a Data Science Course in Kolkata, a Data Analytics Course in Kolkata, or a comprehensive Artificial Intelligence in Data Science Course in Kolkata, this course blends it all in one powerful curriculum. Ideal for students, working professionals, and tech enthusiasts, our Data Science and AI Course Kolkata ensures you gain in-demand skills to thrive in today’s data-driven world.

Backed by expert faculty, real-time projects, and placement support, Ascent Infotech proudly stands as the Best Data Science Institute in Kolkata. If you’re searching for a reliable Data Science & AI course near me, your journey begins right here with us!

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

Topics Covered:

1. What is Data Science?

    • Definition, applications, and industry use cases
    • Data Science vs. AI vs. Machine Learning vs. Big Data

2. Data Science Lifecycle

  • Problem definition, data collection, cleaning, exploration, modeling, deployment

3. Roles in Data Science

  • Data Scientist, Data Analyst, ML Engineer, Data Enginee
  •  

4. Tools & Technologies Overview

  • Python,  SQL, Jupyter Notebooks, Git

5. Setting Up the Environment

  • Anaconda, VS Code, Google Colab

Module 2: Python Programming for Data Science

Python for Data Science

1. Python Basics

  • Variables, loops, functions, OOP

2. Data Structures

  • Lists, dictionaries, NumPy arrays, Pandas DataFrames

3. Data Manipulation

  • Pandas (filtering, grouping, merging)

4. Data Visualization

  • Matplotlib, Seaborn, Plotly

Module 3: Mathematics & Statistics for Data Science

1. Linear Algebra

    • Vectors, matrices, operations, eigenvalues

2. Probability

  • Bayes’ Theorem, distributions (Normal, Binomial, Poisson)

3. Descriptive Statistics

  • Mean, median, variance, skewness, kurtosis

4. Inferential Statistics

    • Hypothesis testing, p-values, confidence intervals

5. Statistical Modeling

  • Regression, ANOVA

Module 4: Data Collection & Preprocessing

1. Data Sources

  • APIs, Web Scraping (BeautifulSoup, Scrapy), Databases

2. Data Cleaning

    • Handling missing values, outliers, duplicates

3.Feature Engineering

    • Encoding, scaling, normalization, PCA

4. Exploratory Data Analysis (EDA)

  • Univariate & bivariate analysis, correlation

Module 5: Machine Learning Fundamentals

Supervised Learning

1. Regression Models

  • Linear, Polynomial, Ridge, Lasso

2. Classification Models

  • Logistic Regression, Decision Trees, SVM, Naïve Bayes

3. Model Evaluation

  • Accuracy, Precision, Recall, F1-Score, ROC-AUC

Unsupervised Learning

1. Clustering

  • Accuracy, Precision, Recall, F1-Score, ROC-AUC

2. Dimensionality Reduction

    • PCA, t-SNE

Ensemble Methods

    • Random Forest, Gradient Boosting (XGBoost, LightGBM)

Module 6: Deep Learning & AI

1. Neural Networks Basics

    • Perceptrons, activation functions, backpropagation

2. Deep Learning Frameworks

      • TensorFlow, Keras, PyTorch

3. Convolutional Neural Networks (CNNs)

      • Image classification

4. Recurrent Neural Networks (RNNs)

    • Time series, NLP

5. Transformers & BERT

      • Advanced NLP applications

Module 7: Natural Language Processing (NLP)

1. Text Preprocessing

    • Tokenization, Lemmatization, Stopwords Removal

2. Text Representation

    • Bag of Words, TF-IDF, Word Embeddings (Word2Vec, GloVe)

3. NLP Models & Techniques

    • Sentiment Analysis, Text Classification, Topic Modeling

Module 8: Data Visualization & Storytelling

4. Visualization Tools

  • Tableau, Power BI, Matplotlib, Seaborn

5. Dashboarding

    • Interactive dashboards

6. Storytelling with Data

  • Presenting insights effectively

Module 9: Deployment & MLOps

1. Model Deployment

  • Flask, FastAPI, Docker

2. MLOps Basics

  • CI/CD, ML pipelines (Airflow, Kubeflow)

3. Model Monitoring

  • Drift detection, performance tracking

Module 10: Capstone Project & Industry Applications

1. End-to-End Project

  • Problem statement, data collection, modeling, deployment

2. Industry Case Studies

  • Healthcare, Finance, E-commerce, Io

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