Data Science Training Course
- Offered bySLA Consultants India
Data Science Training Course at SLA Consultants India Overview
Duration | 150 hours |
Mode of learning | Online |
Credential | Certificate |
Data Science Training Course at SLA Consultants India Highlights
- Earn a certificate after successful completion
- Placement Assistance
- Industry Expert Sr. Lead Analyst / Technical Analyst With 10+ Years
- Real time projects and best case study makes SLA workshop
Data Science Training Course at SLA Consultants India Course details
- Data Science refers to a quantitative and qualitative method and process which is used to increase the productivity and business profitability
- It is a technique of extracting, acknowledging and analyzing information such as behavioral data, business patterns, and techniques which are dynamic and necessary for business
- Every business organization needs to perform Data Science which can provide various benefits such as increased customer satisfaction, enhancing the productivity and performance of the organization and can also provide the companies with the biggest growth opportunities
- Data science is also considered an internal function of any business organization which deals with numbers and figures. Intercourse deep knowledge of recording and analyzing along with dissecting information and presenting the findings to make better decisions making for the management
- However, in order to become a Data Science professional, one should also have knowledge in various Data Science tools such as Python, Power BI R-Programming, MS Excel and Access, Visual Basic for Application and Macros(VBA/Macros), SQL, Tableau and Business Intelligence tools
Data Science Training Course at SLA Consultants India Curriculum
Module 1
Python overview
Advantages and Disadvantages
Installation and configuration
Overview of Programming with Python
What is Python script
Basic of Standalone scripts under Unix and Windows
Variables and Operators
Command line parameters
Understanding expressions
Module 2
Population and sample
Descriptive and Inferential Statistics
Statistical data analysis
Variables
Sample and Population Distributions
Interquartile range
Central Tendency
Normal Distribution
Skewness.
Boxplot
Five Number Summary
Standard deviation
Standard Error
Emperical Formula
Central limit theorem
Estimation
Confidence interval
Hypothesis testing
P-value
Scatterplot and correlation coefficient
Standard Error
Scales of Measurements and Data Types
Data Summarization
Visual Summarization
Numerical Summarization
Outliers and Summary
Module 3
Introduction to Programming
Why Python?
Why Jupyter?
Installing Python and Jupyter
Understanding Jupyter's Interface -the Notebook Dashboard
Prerequisites for Coding in the Jupyter Notebooks
Jupyter's Interface
Python 2 vs Python 3
Module 4 -Tableau / MS Power BI
Getting Started with Tableau
Overview Of Tableau
Tableau Architecture
Installation And Configuration Of Tableau 10
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