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Data Science and Analytics: From Data to Decision-Making

practical and interdisciplinary course designed to equip students with the skills needed to collect, process, analyze, and interpret data to support real-world decision-making. The course covers the complete data lifecycle—from raw data collection to generating actionable insights using statistics, machine learning, and visualization tools. Students will learn how to transform structured and unstructured data into meaningful information using programming, statistical analysis, predictive modelling, and business intelligence techniques. Why Should You Opt for This Course? • Data Science and Analytics professionals are among the most in-demand globally, offering excellent job opportunities and long-term career stability. • The course trains you to convert raw data into meaningful insights, enabling smart and accurate decision-making in real-world situations. •Students gain practical experience with industry-standard tools like Python, SQL, and data visualization platforms through real-life projects. • The skills learned are applicable across sectors such as healthcare, finance, marketing, IT, government, and research. • The course builds a solid base for advanced studies in Data Science, AI, Machine Learning, and Business Analytics.

4.3★★★✰✰

Duration

2 Weeks

Skill Level

Moderate

Starts From:

09/02/2026

Category

SAGE Winter School 2025-26
  • To introduce the fundamental concepts of Data Science and Analytics, enabling students to understand how data is collected, processed, and transformed into useful information.
  • To develop strong analytical and statistical skills required for exploring, summarizing, and interpreting large and complex datasets.
  • To provide hands-on experience with data analysis tools and programming languages such as Python, R, SQL, and data visualization platforms.
  • To train students in applying machine learning techniques for predictive analysis and intelligent decision-making.
  • To enable students to design data-driven solutions for real-world problems across various domains such as business, healthcare, finance, and engineering.
  • To cultivate critical thinking and problem-solving abilities using data as the primary decision-support tool.
  • To familiarize students with ethical, legal, and social issues in data usage, including data privacy, security, and responsible AI.

To prepare students for careers and higher studies in Data Science, Artificial Intelligence, Business Analytics, and related fields.

 

What you'll learn


Syllabus of the course

(Module wise with hrs )

Module 1: Introduction to Data Science (3 Hours)

Topics:

  • What is Data Science?
  • Data Scientist roles & industry applications
  • Data types: structured, semi-structured, unstructured
  • Introduction to analytics: Descriptive, Diagnostic, Predictive, Prescriptive
  • Tools overview: Python, Jupyter, Excel, Power BI
  • Data Science workflow: CRISP-DM

Hands-On:

  • Exploring datasets in CSV/Excel
  • Setting up Python environment

 

Module 2: Data Handling & Preprocessing (6 Hours)

Topics:

  • Importing and exploring data
  • Data cleaning: missing values, outliers, duplicates
  • Data transformation (scaling, encoding, normalization)
  • Feature engineering basics
  • Data wrangling using Pandas & NumPy

Hands-On:

  • Real-world dataset cleaning
  • Creating pipelines for preprocessing

 

Hours Breakdown:

Theory: 2 hours

Hands-on: 4 hours

 

Module 3: Exploratory Data Analysis (EDA) & Visualization (5 Hours)

Topics:

  • Understanding distributions
  • Univariate & multivariate analysis
  • Correlation & covariance
  • Outlier detection
  • Visualization tools: Matplotlib, Seaborn, Power BI/Tableau
  • Insights generation for decision-making

Hands-On:

  • EDA on Kaggle dataset
  • Storytelling using visualization dashboards

 

Hours Breakdown:

Theory: 2 hours

Hands-on: 3 hours

 

Module 4: Machine Learning Foundations (7 Hours)

Topics:

  • ML pipeline: training, testing, validation
  • Supervised vs. Unsupervised learning
  • Regression Models: Linear, Multiple, Polynomial
  • Classification Models: Logistic Regression, Decision Trees, KNN
  • Clustering: K-Means
  • Model evaluation: Accuracy, Precision, Recall, F1, ROC

Hands-On:

  • Build a regression model (Predict house price)
  • Build a classification model (Predict churn)
  • Build a clustering model (Segment customers)

 

Hours Breakdown:

Theory: 3 hours

Practical: 4 hours

 

Module 5: Applied Analytics for Business Decisions (5 Hours)

Topics:

  • Business problem framing
  • KPI development
  • Predictive analytics for decision support
  • A/B Testing basics
  • Data storytelling for stakeholders
  • Case studies: finance, healthcare, e-commerce

Hands-On:

  • Create KPI dashboards
  • Run A/B testing simulation
  • Turn model outputs into business insights

 

Hours Breakdown:

Theory: 2 hours

Practical: 3 hours

 

Module 6: Capstone Project + Review (4 Hours)

Capstone Project:

  • Learners complete an end-to-end mini project:
  • Problem formulation
  • Data cleaning & preprocessing
  • EDA & visualization
  • Model development
  • Insight generation & decision recommendations

Review & Assessment:

MCQs + practical evaluation

  • Feedback session
  • Next steps in career: roadmaps & certifications

Day & Date

Duration (time)

Session

Topic

Resource Person

Monday

09 Feb, 2026
  1. Hour

(01:00 Pm-03:00 pm)

 
  • What is Data Science?
  • Data Scientist roles & industry applications
  • Data types: structured, semi-structured, unstructured
  • Introduction to analytics: Descriptive, Diagnostic, Predictive, Prescriptive

Mr. Ramnath Narhete

Tuesday

10 Feb, 2026
  1. Hour

(01:00 Pm-03:00 pm)

 
  • Tools overview: Python, Jupyter, Excel, Power BI
  • Data Science workflow: CRISP

 

DM Hands-On:

  • Exploring datasets in CSV/Excel
  • Setting up Python environment

Mr. Ramnath Narhete

Wednesday

11 Feb, 2026

02 Hour

 (01:00 pm- 03: 00 pm)

 
  • Importing and exploring data
  • Data cleaning: missing values, outliers, duplicates
  • Data transformation (scaling, encoding, normalization)
  • Feature engineering basics
  • Data wrangling using Pandas & NumPy

Mr. Ramnath Narhete

Thursday

  1. Feb, 2026

02 Hour

(01:00 pm- 03: 00 pm)

 

 

Hands-On:

  • Real-world dataset cleaning
  • Creating pipelines for preprocessing

Mr. Ramnath Narhete

Friday

13 Feb, 2026

02 Hour

(01:00 pm- 03: 00 pm)

 

 
  • Understanding distributions
  • Univariate & multivariate analysis
  • Correlation & covariance
  • Outlier detection
  • Visualization tools: Matplotlib, Seaborn, Power BI/Tableau
  • Insights generation for decision-making

Mr. Ramnath Narhete

Monday

16 Feb, 2026

02 Hour

(01:00 pm- 03: 00 pm)

 

 

Hands-On:

  • EDA on Kaggle dataset
  • Storytelling using visualization dashboards

Mr. Ramnath Narhete

Tuesday

17 Feb, 2026

02 Hour

(01:00 pm- 03: 00 pm)

 

 
  • ML pipeline: training, testing, validation
  • Supervised vs. Unsupervised learning
  • Regression Models: Linear, Multiple, Polynomial
  • Classification Models: Logistic Regression, Decision Trees, KNN
  • Clustering: K-Means
  • Model evaluation: Accuracy, Precision, Recall, F1, ROC

Mr. Ramnath Narhete

Wednesday

18 Feb, 2026

02 Hour

(01:00 pm- 03: 00 pm)

 

 

Hands-On:

  • Build a regression model (Predict house price)
  • Build a classification model (Predict churn)
  • Build a clustering model (Segment customers)

Mr. Ramnath Narhete

Thursday

19 Feb, 2026

02 Hour

(01:00 pm- 03: 00 pm)

 

 
  • Business problem framing
  • KPI development
  • Predictive analytics for decision support
  • A/B Testing basics
  • Data storytelling for stakeholders
  • Case studies: finance, healthcare, e-commerce

Mr. Ramnath Narhete

Friday

20 Feb, 2026

02 Hour

(01:00 pm- 03: 00 pm)

 

 

Hands-On:

  • Create KPI dashboards
  • Run A/B testing simulation
  • Turn model outputs into business insights

Review & Assessment:

MCQs + practical evaluation

  • Feedback session

Next steps in career: roadmaps & certifications

 

Mr. Ramnath Narhete

 

 

 

 

 

 

 

 

 

 
  • Understand and explain the fundamental concepts of Data Science, Data Analytics, and the complete data lifecycle.
  • Apply statistical techniques to analyze and interpret structured and unstructured datasets.
  • Use programming tools such as Python/R and SQL for data collection, cleaning, transformation, and analysis.
  • Create meaningful data visualizations and dashboards to effectively communicate insights and trends.
  • Build and evaluate machine learning models for predictive analytics and decision-making.

Demonstrate the ability to solve real-world problems using data-driven approaches while considering ethical and societal issues in data usage.

 

Name: Monika Jhapate

Mobile No: 9174806226

Email: [email protected]

 

 
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This Premium course is included in plans

₹ 1000/-

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