Data Analytics Training for Data Science and Predictive Modeling

This Data Analytics Training course examines how raw data is prepared, explored, and interpreted to support evidence-based decisions. It connects core analytical methods with data science concepts, including statistical analysis, data visualization, predictive models, and machine learning techniques. Learners build an understanding of how to identify patterns, assess results, and communicate findings across practical business and technical contexts.
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Data Analytics And Data Science Training expands your network through collaborations with leading analytics firms. Participants receive access to proprietary datasets from two Fortune‑500 partners.

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Nishant Kaushik
SAP MM
manideepak Upskilled
manideepak
Manager
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Hari Shankar Jha
Senior System Admin
alok kumar singh Upskilled
alok kumar singh
sap hcm
Jeevak Placed
Jeevak
Consultant
Abhijit S. Getme Upskilled
Abhijit S. Getme
Consultant Engineer
Kartik Singh Placed
Kartik Singh
SAP SD
Hemant Khainar Upskilled
Hemant Khainar
SAP MM Consultant
Neeraj Khanna Placed
Neeraj Khanna
SAP MM
Burna Vinay Kumar Placed
Burna Vinay Kumar
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Shubham Placed
Shubham
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Shashikant Career switch
Shashikant
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Khushbu
Sap hcm
Curriculum

Data Analytics Training, Data Science Course, ZeblearnIndia, predictive models, machine learning techniques Course Syllabus Structure

Data Analytics And Data Science Training covers the full data lifecycle from ingestion to visualization. You will build end‑to‑end pipelines using Apache Spark and Power BI in lab exercises.

  • Introduction to the Course
  • Course Help and Welcome
  • Updates to Notebook Zip
  • Jupyter Notebooks
  • Optional: Virtual Environments
  • Data Science and Business Buzzwords: Why are there so Many?  
  • Data Science and Business Buzzwords: Why are there so Many?
  • What is the difference between Analysis and Analytics
  • What is the difference between Analysis and Analytics
  • Business Analytics, Data Analytics, and Data Science: An Introduction 
  • Business Analytics, Data Analytics, and Data Science: An Introduction
  • Continuing with BI, ML, and AI
  • Continuing with BI, ML, and AI
  • A Breakdown of our Data Science Infographic
  • A Breakdown of our Data Science Infographic
  • Techniques for Working with Traditional Data
  • Techniques for Working with Traditional Data
  • Real Life Examples of Traditional Data
  • Techniques for Working with Big Data
  • Techniques for Working with Big Data
  • Real Life Examples of Big Data
  • Business Intelligence (BI) Techniques
  • Business Intelligence (BI) Techniques
  • Real Life Examples of Business Intelligence (BI)
  • Techniques for Working with Traditional Methods
  • Techniques for Working with Traditional Methods
  • Real Life Examples of Traditional Methods
  • Machine Learning (ML) Techniques
  • Machine Learning (ML) Techniques
  • Types of Machine Learning
  • Types of Machine Learning
  • Real Life Examples of Machine Learning (ML)
  • Real Life Examples of Machine Learning (ML)
  • The Basic Probability Formula
  • The Basic Probability Formula
  • Computing Expected Values
  • Computing Expected Values
  • Frequency
  • Frequency
  • Events and Their Complements
  • Events and Their Complements
  • Types of Data
  • Types of Data
  • Levels of Measurement
  • Levels of Measurement
  • Categorical Variables - Visualization Techniques
  • Categorical Variables - Visualization Techniques
  • Categorical Variables Exercise
  • Numerical Variables - Frequency Distribution Table
  • Numerical Variables - Frequency Distribution Table
  • Numerical Variables Exercise
  • The Histogram
  • The Histogram
  • Histogram Exercise
  • Cross Tables and Scatter Plots
  • Cross Tables and Scatter Plots
  • Cross Tables and Scatter Plots Exercise
  • Mean, Median, and Mode
  • Mean, Median, and Mode Exercise
  • Skewness
  • Skewness
  • Skewness Exercise
  • Variance
  • Variance Exercise
  • Standard Deviation and Coefficient of Variation
  • Standard Deviation
  • Standard Deviation and Coefficient of Variation Exercise
  • Covariance
  • Covariance
  • Covariance Exercise
  • Correlation Coefficient
  • Correlation
  • Introduction to the World of Business and Data
  • Relevant Terms Explained
  • Data Analyst Compared to Other Data Jobs
  • Data Analyst Job Description
  • Why Python
  • Beyond Learning

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    StatusDateTypeScheduleTime
    SOLD OUT 30 Aug 2026 Weekend SAT - SUN (08 Weeks) 18:00 - 20:00
    FILLING FAST 4 Sep 2026 Weekday MON - FRI (08 Weeks) 08:00 - 10:00
    AVAILABLE 9 Sep 2026 Weekend SAT - SUN (08 Weeks) 10:00 - 12:00
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    FAQ

    Frequently Asked Questions

    Q: What core skills are taught in Data Analytics Training? A: Data Analytics Training teaches statistical analysis, data cleaning, and visualization using tools like Python and Tableau. Q: How does Data Analytics Training prepare learners for building predictive models? A: The curriculum includes machine learning techniques and hands‑on projects that create regression and classification models. Q: Is the Data Analytics Training suitable for beginners with no coding background? A: Yes, the program starts with fundamental concepts and step‑by‑step coding exercises in R and SQL.

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