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Data Science

Learn Data Science step by step through real Analytics examples. Data Mining, Modeling, Tableau Visualization and more!
For IT & Computer science Engineers

Course Type :

₹ 29999

Introduction to data analysis and visualization techniques.
Statistical methods for data-driven decision-making.
Machine learning fundamentals and algorithms.
Data preprocessing and cleaning strategies.
Big data handling with tools like Hadoop and Spark.

Predictive modeling and regression analysis.
Clustering and classification techniques.
Natural language processing (NLP) basics.
Real-world project experience and applications.
Exploratory data analysis (EDA) principles.

Course Content

Topic 1 | Introduction to Data Science

Topic 2 | Measure of Central Tendencies, Spread & Dispersion

Topic 3 | Probability Distribution

Topic 4 | Normal & Poisson distribution

Topic 5 | Working with dataset and packages

Topic 6 | SAS Installation & Introduction

Topic 7 | Measure of Central Tendencies

Topic 8 | Estimation and hypothesis testing

Topic 9 | Correlation in SAS

Topic 10 | ANOVA Test

Topic 11 | Internet Survey Case Study

Topic 12 | Regression

Topic 13 | Linear regression and Multi linear regression in SAS

Topic 14 | Logistic regression

Topic 15 | Cluster Analysis

Topic 16 | Time series & Forecasting

Topic 17 | Forecasting in SAS

Topic 18 | Advance Excel

Topic 19 | F- Test

Topic 20 | F- Test & T - Test

Topic 21 | Correlation & Regression

Topic 22 | Exponential Smoothing

Topic 23 | Project

Course Description

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. The ideal models are meant for creating the processes, designing advanced systems and exploring the theories in a better way. Data Science has created a new world of opportunities and make you see the ‘big picture’ of data.

Course objectives

Provide Insights About the Roles of a Data Scientist.
Enable You to Analyze of Big Data.
Learn Techniques and Tools for Transformation of Data.
Make You Understand Data Mining

Roles in industry

Once can aim at becoming a Business Intelligence Analyst
SAS Data Analyst
IBM Data Analyst
Data Scientist
Data Mining Engineer
Machine Learning Engineer
Big Data Scientist
Data Architect
Business Intelligence Architect
Enterprise Data Architect
Big Data Architect
Hadoop Engineer
Data Warehouse Architect
Senior Data Scientist

Course Highlights

Data Analytics & its Methodology
Fundamental of statistics
Data Visualization
Data Distribution & Correlation
Test of significance – Hypothesis testing, t-Test, ANOVA
Data Mining – Unsupervised & Supervised
Regression Analysis - Linear & Logistic
Cluster Analysis – Hierarchical & k-Means
Time series Analysis
Text Mining

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Data Science

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Data Science

Course Content

Topic 1 | Introduction To Data Science (2 lectures)

Topic 2 | Measure Of Central Tendencies, Spread & Dispersion (2 lectures)

Topic 3 | Probability Distribution (2 lectures)

Topic 4 | Normal & Poisson Distribution (2 lectures)

Topic 5 | Working With Dataset And Packages (2 lectures)

Topic 6 | Sas Installation & Introduction (2 lectures)

Topic 7 | Measure Of Central Tendencies (2 lectures)

Topic 8 | Estimation And Hypothesis Testing (2 lectures)

Topic 9 | Correlation In Sas (2 lectures)

Topic 10 | Anova Test (2 lectures)

Topic 11 | Internet Survey Case Study (2 lectures)

Topic 12 | Regression (2 lectures)

Topic 13 | Linear Regression And Multi Linear Regression In Sas (2 lectures)

Topic 14 | Logistic Regression (2 lectures)

Topic 15 | Cluster Analysis (2 lectures)

Topic 16 | Time Series & Forecasting (2 lectures)

Topic 17 | Forecasting In Sas (2 lectures)

Topic 18 | Advance Excel (2 lectures)

Topic 19 | F- Test (2 lectures)

Topic 20 | F- Test & T - Test (2 lectures)

Topic 21 | Correlation & Regression (2 lectures)

Topic 22 | Exponential Smoothing (2 lectures)

Session 23 (1 lectures)

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