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Data Science & AI

DataScience & Gen AI Curriculum

Master Data Science & Gen AI skills. Learn Python, ML, and LLMs through real projects. Build intelligent apps and become AI career-ready with SkillSprint.

26 Weeks

Duration

Python with Flask & SQL Course

Advance Your Career with SkillSprintTech’s Data Science & Gen AI Curriculum in Pune

Are you looking to future-proof your career? Our Data Science and Gen AI Curriculum is strategically designed keeping in mind to prepare students as well as professionals for high-growth opportunities in data-driven industries of today’s time. No matter if you are a fresh graduate or an IT professional aiming to upskill and get access to better opportunities, SkillSprintTech is the right place to initiate your journey.

Why Should You Choose SkillSprintTech?

We are not just another data science training institute in Pune. At SkillSprintTech, our expert team puts in special efforts to blend practical learning with industry-ready tools to give you a real-world edge that makes all the difference. Our program is not merely about theories — it is about hands-on projects, simulative sessions, various live tools, & mentorship from data science practitioners.


What Makes Our Data Science Course in Pune Stand Out?

  • Industry-Aligned Curriculum: We offer the best data science course in Pune, combining the various core modules in Python, SQL, & Machine Learning with several advanced topics like NLP, Gen AI, & Deep Learning.
  • Hands-On Training: Learn by doing. Work on capstone projects, real-time datasets, & industry use cases to build a practical skillset.
  • Multiple Modes of Learning: Prefer flexibility? We also provide data science online training in Pune. Get the right access to the same quality instruction, resources, as well as full online support.

Effective Placement Support That Works

We thoroughly understand your end goal is not just learning—it is landing a job. That is why our program is also well-known as the best data science course in Pune with placement. We offer resume-building sessions, mock interviews, & direct placement assistance with top companies.


Affordable Course Fees

If you are concerned about data science course fees in Pune, do not worry. Our pricing is completely transparent & absolutely affordable with the option of paying in EMIs as well. We also provide early-bird discounts & group offers.


Certified & Recognized Training

Earn a data science certification course in Pune that is well-recognized by industry recruiters. Our certificate validates your skill level & helps you stand out in a crowded job market.


What Will You Learn in the Course?

  • Python Programming for Data Analysis
  • Statistics & Data Wrangling
  • SQL & Databases
  • Machine Learning Algorithms
  • Generative AI & Large Language Models (LLMs)
  • Power BI / Tableau for Data Visualization
  • Model Deployment & MLOps

Course Curriculum

1

Module 1: Introduction to Data Science

  • Overview of data science, its applications, and the data science process.
  • Overview of data science, its applications, and the data science process.
  • Learning Outcome: Understand the basics of data science and its role in various industries.
  • Real-World Application: Learn how data science is used in personalized recommendations for e- commerce websites.
2

Module 2: Data Collection and Preprocessing

  • Techniques for collecting data from various sources (databases, APIs, web scraping
  • Data cleaning and preprocessing methods, including handling missing values, outliers, and data transformation.
  • Learning Outcome: Master data collection and preparation techniques.
  • Real-World Application: Learn how to clean and prepare customer data for marketing analysis.
3

Module 3: Introduction to Python

  • Introduction to the Python programming language, including data types, control flow, functions, and modules.
  • Learning Outcome: Become proficient in basic Python programming.
  • Learning Outcome: Become proficient in basic Python programming.
4

Module 4: Data Manipulation with Pandas

  • Using the Pandas library for data manipulation, cleaning, and analysis.
  • Working with DataFrames, handling missing data, and performing data aggregation.
  • Learning Outcome: Effectively manipulate and analyze data using Pandas.
  • Real-World Application: Analyze sales data to identify top-performing products and customer segments.
5

Module 5: Data Visualization with Matplotlib and Seaborn

  • Creating informative and visually appealing charts and graphs using Matplotlib and Seaborn.
  • Customizing plots, exploring different visualization techniques, and communicating data insights.
  • Learning Outcome: Visualize data effectively to identify trends and patterns.
  • Real-World Application: Create visualizations to present survey results to stakeholders.
6

Module 6: Statistical Analysis

  • Fundamentals of statistical analysis, including descriptive statistics, probability distributions, hypothesis testing, and regression analysis.
  • Learning Outcome: Apply statistical methods to analyze data and draw meaningful conclusions.
  • Real-World Application: Perform A/B testing to determine the effectiveness of different marketing campaigns.
7

Module 7: SQL for Data Science

  • Introduction to SQL for querying and manipulating data in relational databases.
  • Writing SQL queries to extract, filter, and aggregate data for analysis.
  • Learning Outcome: Retrieve and manipulate data from databases using SQL.
  • Real-World Application: Extract customer transaction data from a database for fraud detection analysis.
8

Module 8: Machine Learning Fundamentals

  • Overview of machine learning concepts, including supervised learning, unsupervised learning, and reinforcement learning.
  • Introduction to common machine learning algorithms and their applications.
  • Learning Outcome: Understand the basics of machine learning and its different paradigms.
  • Real-World Application: Identify use cases for machine learning in various industries.
9

Module 8: Machine Learning Fundamentals

  • Overview of machine learning concepts, including supervised learning, unsupervised learning, and reinforcement learning.
  • Introduction to common machine learning algorithms and their applications.
  • Learning Outcome: Understand the basics of machine learning and its different paradigms.
  • Real-World Application: Identify use cases for machine learning in various industries.
10

Module 9: Linear Regression

  • Building and evaluating linear regression models for predicting continuous variables. Understanding model assumptions, interpreting coefficients, and assessing model performance.
  • Learning Outcome: Build and interpret linear regression models.
  • Real-World Application: Predict housing prices based on features like size, location, and amenities.
11

Module 10: Logistic Regression

  • Building and evaluating logistic regression models for binary classification problems. Understanding odds ratios, interpreting coefficients, and assessing model performance.
  • Learning Outcome: Build and interpret logistic regression models.
  • Real-World Application: Predict customer churn based on their behavior and demographics.
12

Module 11: Decision Trees

  • Introduction to decision trees, including building, visualizing, and interpreting decision tree models.
  • Understanding concepts like entropy, information gain, and pruning.
  • Learning Outcome: Build and interpret decision tree models.
  • Real-World Application: Build a decision tree to classify loan applications as high-risk or low-risk.
13

Module 12: Random Forests

  • Module 12: Random Forests
  • Learning Outcome: Build and optimize random forest models.
  • Real-World Application: Predict customer purchase behavior using a random forest model.
14

Module 13: Support Vector Machines (SVM)

  • Introduction to Support Vector Machines (SVM) for classification regression tasks. Understanding kernels, margins, and support vectors.
  • Learning Outcome: Understand and apply Support Vector Machines.
  • Real-World Application: Classify images using an SVM model.
15

Module 14: K-Means Clustering

  • Unsupervised learning with K-Means clustering. Understanding the K- Means algorithm, choosing the optimal number of clusters, and interpreting cluster results.
  • Learning Outcome: Apply K-Means clustering to segment data.
  • Real-World Application: Segment customers based on their purchasing patterns.
16

Module 15: Dimensionality Reduction with PCA

  • Reducing the dimensionality of data using Principal Component Analysis (PCA).
  • Understanding the PCA algorithm and interpreting the principal components.
  • Learning Outcome: Reduce the dimensionality of data using PCA.
  • Learning Outcome: Reduce the dimensionality of data using PCA.
17

Module 16: Model Evaluation and Selection

  • Metrics for evaluating machine learning models (accuracy, precision, recall, F1-score, AUC).
  • Techniques for model selection, including cross- validation and hyperparameter tuning.
  • Learning Outcome: Evaluate and select the best machine learning model for a given task.
  • Real-World Application: Compare the performance of different models for predicting customer churn.
18

Module 17: Natural Language Processing (NLP) Basics

  • Introduction to Natural Language Processing (NLP).
  • Text preprocessing techniques, sentiment analysis, and topic modeling.
  • Learning Outcome: Understand the basics of NLP and its applications.
  • Real-World Application: Perform sentiment analysis on customer reviews to understand customer satisfaction.
19

Module 18:Time Series Analysis

  • Analyzing time series data. Time series decomposition, forecasting techniques (ARIMA, Exponential Smoothing).
  • Learning Outcome: Analyze and forecast time series data.
  • Real-World Application: Forecast sales for the next quarter based on historical sales data.
20

Module 19: Big Data Technologies

  • Introduction to Big Data technologies like Hadoop and Spark.
  • Understanding the challenges of big data and how these technologies address them.
  • Learning Outcome: Understand the basics of Big Data technologies.
  • Real-World Application: Process large datasets using Spark.
21

Module 20: Cloud Computing for Data Science

  • Using cloud platforms (AWS, Azure, GCP) for data science tasks. Deploying machine learning models on the cloud.
  • Learning Outcome: Deploy data science solutions on the cloud.
  • Real-World Application: Deploy a machine learning model on AWS SageMaker.
22

Module 21: Data Ethics and Privacy

  • Ethical considerations in data science. Data privacy regulations (GDPR, CCPA). Bias in machine learning models.
  • Learning Outcome: Understand ethical considerations in data science.
  • Real-World Application: Identify and mitigate bias in a machine learning model.
23

Module 22: Data Storytelling and Communication

  • Communicating data insights effectively. Creating data visualizations for presentations.
  • Writing data-driven reports.
  • Writing data-driven reports.
  • Real-World Application: Present data findings to a non-technical audience.
24

Module 23: Generative AI & Prompt Engineering

  • Learn the fundamentals of Generative AI and Large Language Models like ChatGPT and Gemini, and explore how they can be used to build intelligent text-based applications.
  • This module covers prompt engineering techniques, hands-on usage of OpenAI APIs, and ethical considerations like hallucinations and bias.
  • Learning Outcome: Understand and apply Gen-AI tools to real tasks with confidence.
  • Real-World Application: Build a mini-project such as an AI-powered Resume Generator, FAQ Bot, or Content Assistant using prompts and APIs.
25

Module 24: Building a Data Science Portfolio & Job Search Strategies

  • Creating a portfolio of data science projects to showcase your skills.
  • Building a personal website or GitHub repository .Strategies for finding data science jobs.
  • Resume writing, interview preparation, and networking.
  • Learning Outcome: Prepare for a data science job search.
  • Real-World Application: Practice answering common data science interview questions.
26

Module 25: Capstone Project

  • A comprehensive data science project that integrates all the skills and knowledge learned throughout the course.
  • Learning Outcome: Apply data science skills to solve a real-world problem.
  • Real-World Application: Complete a capstone project that can be showcased to potential employers.

Frequently Asked Questions

Anyone who is interested in entering or growing in the field of data science—freshers, analysts, software engineers, or marketing professionals.
Not at all. This course begins with Python basics & progresses gradually.
Data Analyst, Data Scientist, Machine Learning Engineer, AI Engineer, Business Intelligence Analyst, & more.
Both! Choose between our in-person training at the data science training institute in Pune or you may opt for data science online training in Pune. You may choose the option that suits you the best.
Yes, we offer end-to-end placement support including mock interviews, job referrals, & resume building.

Join Our Community of Data Enthusiasts

No matter if you are comparing institutes or searching for the best data science course in Pune, SkillSprintTech offers everything that you need for an effective training - expert trainers, hands-on projects, flexible modes of learning, & a strong placement record.Join a community of data enthusiasts who are drastically transforming their careers. Upskill today & become a part of tomorrow’s tech revolution.

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