Data Science Professional

1. Statistical Analysis with Minitab and R

1.1 Introduction to Statistics, EDA

1.2 Types of Data, Measurement Scales

1.3 Measures of Central Tendency, Dispersion

1.4 Case Study on EDA, MCT and MD

1.5 Data Description, Probability, Discrete , Continuous & Sampling Distribution

2. Data Visualization with Tableau 

2.1 What is Data

2.2 Importance of Data Visualization

2.3 Why Tableau?

2.4 Pivot and Split tables, Bar plots, Time Series Charts

2.5 Data Forecasting

2.6 Dual, Blended & Cross Data Axis Chart

2.7 Maps & Filters in Tableau

2.8 Donut, Pareto, Water fall, Bump Chart 

2.9 Tree Maps & Word Cloud

2.10 Real - Time Project Assignment

3. Data Wrangling with R

3.1 Introduction & installing to R Programming & Studio

3.2 Why Data Wrangling in Data Science

3.3 Basic Function in R Programming

3.4 Statistical Analysis with R

3.5 Advanced Data Structures

3.6 Reading Data into R

3.7 Data Reshaping, Transformation

4. Machine Learning with R

4.1 Linear, Logistic Regression

4.2 Decision Tree

4.3 Support Vector Machines

4.4 Choosing of Hyper plane

4.5 Improving the SVM Model accuracy with various Kernel’s

4.6 Text Mining and NLP

4.7 Bag & Stemming of Words

4.8 Document Term Matrix with R and Term Document Matrix

4.9 Positive Word Cloud and Negative Word Cloud

4.10 IPhone 7 Review Analysis

4.11 Naive Bayes Classifiers

4.12 Theory behind Naive Bayes Classifiers

4.13 Bayesian Rule

• Data Science course will help in attaining extensive knowledge in statistical and solving analytical applications
• Data science course will help in enhancing your business intelligence
• This consists of learning modules specific towards the big data
•  Advanced training methodologies
•  Best learning experience through the visual based training program.
•  Practical sessions with the best infrastructure lab faculty.
• Well experienced faculty having in-depth subject knowledge.

•  Anybody with an interest in Data Science
• Anybody who wants to improve their data mining skills
• Anybody who wants to improve their statistical modelling skills
• Anybody who wants to improve their data preparation skills
• Anybody who wants to improve their Data Science presentation skills
• Those who want to become master in data science and data analytics in R programming.
• Business analysts who want to learn machine learning
• Data analysts who want to improve their skills.
• Developers aspiring to become data scientist
• Fresher’s/Experienced Professional, Managers, IT Professional