Data Science Syllabus

There are 8 key modules [as mentioned earlier], which are further divided into nine lessons. These lessons cover a wide range of subjects – programming language, statistical tools, algorithms, and machine learning –
Module 1: Python
Python is a general purpose, dynamic, high-level, and interpreted programming language. It supports Object Oriented programming approach to develop applications. It is simple and easy to learn and provides lots of high-level data structures. Python
Python Features
Python History
Python Applications
Python Install
Python Example
Python Variables
Python Data Types
Python Keywords
Python Literals
Python Operators
Python Comments
Python If else
Python Loops
Python For Loop
Python While Loop
Python Break
Python Continue
Python Pass
Python Strings
Python Lists
Python Tuples
Python Sets
Python Dictionary
Python Functions
Python Lambda Functions
Python Files I/O
Python Exceptions
Python Date
Python Regex
Read CSV File
Write CSV File
Python List Comprehension
Python Random Module
Python Statistics Module

Module 2: Data Visualization
Visualize: We analyze the raw data, which means it makes complex data more accessible, understandable, and more usable. Tabular data representation is used where the user will look up a specific measurement, while the chart of several types is used to show patterns or relationships in the data for one or more variables.
Plot Function
Hist plot
Bar Graph
Pie Graph
Subplot
imread and imshow

Module 3: Data Manipulation
Data manipulation refers to the process of adjusting data to make it organised and easier to read. Data manipulation language, or DML, is a programming language that adjusts data by inserting, deleting and modifying data in a database such as to cleanse or map the data. Numpy
Pandas

Module 4: Machine Learning
machine learning system builds prediction models, learns from previous data, and predicts the output of new data whenever it receives it. The amount of data helps to build a better model that accurately predicts the output, which in turn affects the accuracy of the predicted output.
Machine Learning Introduction
Machine Learning Applications
Life cycle of Machine Learning
Install Anaconda & Python
AI vs Machine LearningH
ow to Get Datasets
Data Preprocessing
Supervised Machine Learning
Unsupervised Machine Learning
Supervised vs Unsupervised Learning
Supervised Learning
Regression Analysis
Linear Regression
Simple Linear Regression
Multiple Linear Regression
Backward Elimination
Polynomial Regression
Classification Algorithm
Logistic RegressionK-NN Algorithm
Support Vector Machine Algorithm
Naïve Bayes Classifier

Module 5: Statistics & Probability
When working with data, the knowledge of statistics is necessary and an important skill set that you must have. In this module, you will learn –
Important statistical concepts used in data science
Difference between population a>nd sample
Types of variables
Measures of central tendency
Measures of variability
Coefficient of variance
Normal distribution
Mean
Mediam
Mod
Standard Deviation
Test hypotheses
Central limit theorem
Confidence interval
T-test
Type I and II errors
Student’s T distribution

Module 6: R Programming
R programming is a very popular language and to work on that we have to install two things, i.e., R and RStudio. R and RStudio works together to create a project on R. Installing R to the local computer is very easy. First, we must know which operating system we are using so that we can download it accordingly.
R Introduction
R Installation
RStudio IDE
R Advantage & Disadvantage
R Hadoop Integration
R Packages
List of R Packages
R Basic Syntax
R Data Types
R Data Structures
R Variables
R Keywords
R Operators
R If Statement
If-else Statement
else if Statement
R Switch Statement
R Next Statement
R Break Statement
R For Loop
R Repeat Loop
R While Loop
R Functions
R Built-in Functions
R Vectors
R Lists
R Arrays
R Matrix
R Data Frame
R Factors

Module 7: Tableau
Tableau is the fastly growing and powerful data visualization tool. Tableau is a business intelligence tool which helps us to analyze the raw data in the form of the visual manner; it may be a graph, report, etc. Tableau software doesn't require any technical or any programming skills to operate. Tableau is easy and fast for creating visual dashboards.
Introduction to Tableau
How to import fike in excel
How to work with excel data
How to formatting charts
How to apply filter excel charts
How to apply function (set Parameter)
How to apply calculation field in charts
How to apply Hierarchy in charts
How to create dashboard in Tableau
How to create story board in Tableau
How to create mapping of charts
How to apply relationship of two or more tables
How to apply joining function in Tableau
How to apply dax function in Tableau
How to apply union function in charts
How to set Paratment in charts
How to apply reference line in charts
How to apply Different validation in chart
How to apply groupin in chart
projects

Module 8: Adv Excel
In one job description, advanced Excel is defined as. Highly proficient with Microsoft office and particularly Excel (i.e. pivot tables, lookups, advanced formulas) In another, it required a greater skill set. Excel advanced functions (macros, index, conditional list, arrays, pivots, lookups)
Customizing Common option in excel
Working with Function
Writing Conditional Statement
Sorting & filtering data
Formatting & Customizing pivot chart
Date & functions
Data Validations
Chart Recommendation
Format Charts
Chart Design
Richer Data Labels
Leader lines
New Functions
Visualization
Pie Chart
Additional Features
Power view in service
Format Report
Handling Integers
Templates
Inquire
Workbook Analysis
Manage password
File Formats
format VLOOKUP,HLOOKUP
creating pivot table ,pivot charts Forms
Working with Macros
Corporate dashboard
Sorting Data by color
slicers
flash fill Pivot table recommendation
Data Model
power pivot
External Data connection
Pivot table tools
Power view
Instant Data Analyst

Module 9: PowerBI
Power BI services are based on SaaS and mobile Power BI apps that are available for different platforms. These set of services are used by the business users to consume data and to build Power BI reports.
Introduction to Power BI
How to import Excel File
How to work with trnsform data
How to apply group by Function
How to apply append query in power BI
How to create dashboard design
How to use mapping chart
How to apply new measure function in power BI
How to apply table function in power BI
How to apply filter effect in charts
How to apply page layout in charts
How to apply custom and condtion column in power BI
How to apply DAX function in Power BI
How to create two or more table of relationship
How to apply bok marks effect in power BI
How to convert Mobile Layout charts
How to apply decomposition tree and smart Narrative
How to apply Q&A charts
How to apply export and import data from file
Projects

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