Data Science & Analytics: Unlock Insights from Data(Offline)

The "Data Science & Analytics" course is a comprehensive, hands-on program designed to teach you how to extract meaningful insights from data and make informed decisions. You’ll gain proficiency in Python programming, data analysis, machine learning, and data visualization, using real-world datasets to apply your knowledge practically.

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Course Schedule and Duration

• Duration: 6 Months
• Start Date: Option to start today also, instructor will adjust you to recently started batch. Enroll now
• Days: Mon-Tue-Wed-Thu-Fri-Sat, 6 days a week and 1.5 Hours/Session
• Timings: 8.00 am to 8.00 pm ( Choose any 1.5 hours)
• Mode: Offline  (Near Radhika's Authentic South Indian Food, Gurukul, Ahmedabad, Gujarat 380054)
• Languages: English, Hindi & Gujatati
• Fees:  INR 42,000/-

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Course Structure

Module 1: Introduction to Data Science & Analytics

• Overview of data science and its applications.
• The data science process: collecting, analyzing, interpreting, and visualizing data.
• Introduction to Python for data analysis.

Module 2: Data Manipulation with Python

• Working with Pandas and NumPy for data wrangling.
• Handling missing data, outliers, and data transformation.
• Data cleaning techniques for large datasets.

Module 3: Data Visualization Techniques

• Creating basic and advanced plots with Matplotlib and Seaborn.
• Dashboard design and interactive data visualizations with Tableau.
• Storytelling with data: turning raw data into actionable insights.

Module 4: Statistical Analysis for Data Science

• Descriptive statistics: mean, median, mode, variance, standard deviation.
• Inferential statistics: hypothesis testing, confidence intervals, correlation.
• Probability distributions and data modeling.

Module 5: Machine Learning Foundations

• Introduction to machine learning: supervised vs. unsupervised learning.
• Regression Analysis: linear and multiple regression models.
• Classification Techniques: logistic regression, decision trees, random forests.
• Clustering Algorithms: K-means clustering, hierarchical clustering.

Module 6: Data Preprocessing & Feature Engineering

• Data normalization and standardization.
• Feature selection, dimensionality reduction (PCA), and encoding categorical variables.
• Dealing with imbalanced datasets.

Module 7: Working with Real-World Datasets

• Case studies: applying machine learning models to business problems.
• End-to-end data analysis projects: from data collection to visualization.
• Practical exercises with open datasets in healthcare, finance, and marketing.

Module 8: Model Evaluation & Optimization

• Model validation techniques: cross-validation, train-test split.
• Performance metrics: accuracy, precision, recall, F1-score, ROC-AUC curve.
• Hyperparameter tuning and model optimization strategies.

Module 9: Deploying Data Science Projects

• Introduction to model deployment using Flask and APIs.
• Automating data workflows for real-time data analytics.
• Basics of cloud platforms for deploying data models.

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Capstone Project

• Develop a comprehensive data science project using real-world data.
• Apply data analysis, machine learning, and visualization skills to solve a business challenge.
• Present your findings through interactive dashboards and reports.

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Why Choose This Course?

• Hands-On Learning: Work with real datasets to build practical skills.
• Industry Tools: Learn Python, Pandas, NumPy, Matplotlib, Seaborn, Tableau, and machine learning frameworks.
• Career-Ready Skills: Prepare for roles such as Data Analyst, Data Scientist, Business Analyst, and more.
• Project Portfolio: Build a portfolio of projects to showcase your data science skills to employers.

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By the end of this course, you’ll have the technical expertise and analytical mindset needed to thrive in the data-driven world. Whether you’re starting a new career or enhancing your current role, this course will equip you with the skills to analyze data, build predictive models, and make data-driven decisions confidently.