Data Science Certification Course

Introduction To Python

• Installation and Working with Python

• Understanding Python variables

• Python basic Operators

• Understanding the Python blocks.

Python Keyword and Identiers

• Python Comments, Multiline Comments.

• Python Indentation

• Understating the concepts of Operators
• Arithmetic
• Relational
• Logical
• Assignment
• Membership
• Identity

Introduction To Variables

• Variables, expression condition and function

• Global and Local Variables in Python

• Packing and Unpacking Arguments

• Type Casting in Python

• Byte objects vs. string in Python

• Variable Scope

Python Data Type

• Declaring and using Numeric data types

• Using string data type and string operations

• Understanding Non-numeric data types

• Understanding the concept of Casting and Boolean.

• Strings

• List

• Tuples

• Dictionary

• Sets

Control Structure & Flow

• Statements – if, else, elif

• How to use nested IF and Else in Python

• Loops

• Loops and Control Statements.

• Jumping Statements – Break, Continue, pass

• Looping techniques in Python

• How to use Range function in Loop

• Programs for printing Patterns in Python

• How to use if and else with Loop

• Use of Switch Function in Loop

• Elegant way of Python Iteration

• Generator in Python

• How to use nested Loop in Python

• Use If and Else in for and While Loop

• Examples of Looping with Break and Continue Statement

• How to use IN or NOT IN keyword in Python Loop.

Python Function, Modules and Packages

• Python Syntax

• Function Call

• Return Statement

• Arguments in a function – Required, Default, Positional, Variable-length

• Write an Empty Function in Python –pass statement.

• Lamda/ Anonymous Function

• *args and **kwargs

• Help function in Python

• Scope and Life Time of Variable in Python Function

• Nested Loop in Python Function

• Recursive Function and Its Advantage and Disadvantage

• Organizing python codes using functions

• Organizing python projects into modules

• Importing own module as well as external modules

• Understanding Packages

• Random functions in python

• Programming using functions, modules & external packages

• Map, Filter and Reduce function with Lambda Function

• More example of Python Function

Python Date Time and Calendar

• Day, Month, Year, Today, Weekday

• IsoWeek day

• Date Time

• Time, Hour, Minute, Sec, Microsec

• Time Delta and UTC

• StrfTime, Now

• Time stamp and Date Format

• Month Calendar

• Itermonthdates

• Lots of Example on Python Calendar

• Create 12-month Calendar

• Strftime

• Strptime

• Format Code list of Data, Time and Cal

• Locale’s appropriate date and time

List

• What is List.

• List Creation

• List Length

• List Append

• List Insert

• List Remove

• List Append & Extend using “+” and Keyword

• List Delete

• List related Keyword in Python

• List Revers

• List Sorting

• List having Multiple Reference

• String Split to create a List

• List Indexing

• List Slicing

• List count and Looping

• List Comprehension and Nested Comprehension

Tuple

• What is Tuple

• Tuple Creation

• Accessing Elements in Tuple

• Changing a Tuple

• Tuple Deletion

• Tuple Count

• Tuple Index

• Tuple Membership

• TupleBuilt in Function (Length, Sort)

Dictionary

• Dict Creation

• Dict Access (Accessing Dict Values)

• Dict Get Method

• Dict Add or Modify Elements

• Dict Copy

• Dict From Keys.

• Dict Items

• Dict Keys (Updating, Removing and Iterating)

• Dict Values

• Dict Comprehension

• Default Dictionaries

• Ordered Dictionaries

• Looping Dictionaries

• Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets

• What is Set

• Set Creation

• Add element to a Set

• Remove elements from a Set

• PythonSet Operations

• Frozen Sets

Strings

• What is Set

• Set Creation

• Add element to a Set

• Remove elements from a Set

• PythonSet Operations

Python Exception Handling

• Python Errors and Built-in-Exceptions

• Exception handing Try, Except and Finally

• Catching Exceptions in Python

• Catching Specic Exception in Python

• Raising Exception

• Try and Finally

Python File Handling

• Opening a File

• Python File Modes

• Closing File

• Writing to a File

• Reading from a File

• Renaming and Deleting Files in Python

• Python Directory and File Management

• List Directories and Files

• Making New Directory

• Changing Directory

Python Database Interaction

• SQL Database connection using

• Creating and searching tables

• Reading and Storing cong information on database

• Programming using database connections

Contacting user Through Emails Using Python

• Installing SMTP Python Module

• Sending Email

• Reading from le and sending emails to all users

Reading an excel

• Working With Excel

• Reading an excel le using Python

• Writing to an excel sheet using Python

• Python| Reading an excel le

• Python | Writing an excel le

• Adjusting Rows and Column using Python

• ArithmeticOperation in Excel le.

• Play with Workbook, Sheets and Cells in Excel using Python

• Creating and Removing Sheets

• Formatting the Excel File Data

• More example of Python Function

Complete Understanding of OS Module of Python

• Check Dirs. (exist or not)

• How to split path and extension

• How to get user prole detail

• Get the path of Desktop, Documents, Downloads etc.

• Handle the File System Organization using OS

• How to get any les and folder’s details using OS

Data Analysis and Visualization using Pandas.

• Statistics
• Categorical Data
• Numerical Data
• Mean
• Median
• Mode
• Outliers
• Range
• Interquartile range
• Correlation
• Standard Deviation
• Variance
• Box plot

• Pandas
• Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
• How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
• Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue’s
• Exploring the Data Plotting, Correlations, and Histograms
• Getting statistical information about the data Analysis Concepts, Handle the None Values
• Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
• Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
• Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
• Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
• Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
• Exporting the results to Excel Joins
• Python | Pandas Data Frame Inner Join
• Under sting the properties of Data Frame Left Join (Left Outer Join)
• Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
• Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
• Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
• Pandas | How to Group Data How to apply Lambda / Function on Data Frame
• Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

Data Analysis and Visualization using NumPy and MatPlotLib

• NumPy
• Introduction to NumPy Numerical Python
• Importing NumPy and Its Properties
• NumPy Arrays
• Creating an Array from a CSV
• Operations an Array from a CSV
• Operations with NumPy Arrays
• Two-Dimensional Array
• Selecting Elements from 1-D Array
• Selecting Elements from 2-D Array
• Logical Operation with Arrays
• Indexing NumPy elements using conditionals
• NumPy’s Mean and Axis
• NumPy’s Mode, Median and Sum Function
• NumPy’s Sort Function and More

• MatPlotLib
• Bar Chart using Python MatPlotLib
• Column Chart using Python MatPlotLib
• Pie Chart using Python MatPlotLib
• Area Chart using Python MatPlotLib
• Scatter Plot Chart using Python MatPlotLib
• Play with Charts Properties Using MatPlotLib
• Export the Chart as Image
• Understanding plt. subplots () notation
• Legend Alignment of Chart using MatPlotLib
• Create Charts as Image
• Other Useful Properties of Charts.
• Complete Understanding of Histograms
• Plotting Different Charts, Labels, and Labels Alignment etc.

Introduction to Data Visualization with Seaborn

• Introduction to Seaborn
• Introduction to Seaborn
• Making a scatter plot with lists
• Making a count plot with a list
• Using Pandas with seaborn
• Tidy vs Untidy data
• Making a count plot with a Dataframe
• Adding a third variable with hue
• Hue and scattera plots
• Hue and count plots

• Visualizing Two Quantitative Variables
• Introduction to relational plots and subplots
• Creating subplots with col and row
• Customizing scatters plots
• Changing the size of scatter plot points
• Changing the style of scatter plot points
• Introduction to line plots
• Interpreting line plots
• Visualizing standard deviation with line plots
• Plotting subgroups in line plots

• Visualizing a Categorical and a Quantitative Variable
• Current plots and bar plots
• Count plots
• Bar plot with percentages
• Customizing bar plots
• Box plots
• Create and interpret a box plot
• Omitting outliers
• Adjusting the whisk
• Point plots
• Customizing points plots
• Point plot with subgroups

• Customizing Seaborn Plots
• Changing plot style and colour
• Changing style and palette
• Changing the scale
• Using a custom palette
• Adding titles and labels Part 1
• Face Grids vs. Axes Subplots
• Adding a title to a face Grid object
• Adding title and labels Part 2
• Adding a title and axis labels
• Rotating x-tics labels
• Putting it all together
• Box plot with subgroups
• Bar plot with subgroups and subplots
• Well done! What’s next

SQL Server Fundamentals

• SQL Server 2019 Installation

• Service Accounts & Use, Authentication Modes & Usage, Instance Congurations

• SQL Server Features & Purpose

• Using Management Studio (SSMS)

• Conguration Tools & SQLCMD

• Conventions & Collation

SQL Server 2019 Database Design

• SQL Database Architecture

• Database Creation using GUI

• Database Creation using T-SQL scripts

• DB Design using Files and File Groups

• File locations and Size parameters

• Database Structure modications

SQL Tables in MS SQL Server

• SQL Server Database Tables

• Table creation using T-SQL Scripts

• Naming Conventions for Columns

• Single Row and Multi-Row Inserts

• Table Aliases

• Column Aliases & Usage

• Table creation using Schemas

• Basic INSERT

• UPDATE

• DELETE

• SELECT queries and Schemas

• Use of WHERE, IN and BETWEEN

• Variants of SELECT statement

• ORDER BY

• GROUPING

• HAVING

• ROWCOUNT and CUBE Functions

Data Validation and Constraints

• Table creation using Constraints

• NULL and IDENTITY properties

• UNIQUE KEY Constraint and NOT NULL

• PRIMARY KEY Constraint & Usage

• CHECK and DEFAULT Constraints

• Naming Composite Primary Keys

• Disabling Constraints & Other Options

Views and Row Data Security

• Benets of Views in SQL Database

• Views on Tables and Views

• SCHEMA BINDING and ENCRYPTION

• Issues with Views and ALTER TABLE

• Common System Views and Metadata

• Common Dynamic Management views

• Working with JOINS inside views

Indexes and Query tuning

• Need for Indexes & Usage

• Indexing Table & View Columns

• Index SCAN and SEEK

• INCLUDED Indexes & Usage

• Materializing Views (storage level)

• Composite Indexed Columns & Keys

• Indexes and Table Constraints

• Primary Keys & Non-Clustered Indexes

Stored Procedures and Benets

• Why to use Stored Procedures

• Types of Stored Procedures

• Use of Variables and parameters

• SCHEMABINDING and ENCRYPTION

• INPUT and OUTPUT parameters

• System level Stored Procedures

• Dynamic SQL and parameterization

System functions and Usage

• Scalar Valued Functions

• Types of Table Valued Functions

• SCHEMABINDING and ENCRYPTION

• System Functions and usage

• Date Functions

• Time Functions

• String and Operational Functions

• ROW_COUNT

• GROUPING Functions

Triggers, cursors, memory limitations

• Why to use Triggers

• DML Triggers and Performance impact

• INSERTED and DELETED memory tables

• Data Audit operations & Sampling

• Database Triggers and Server Triggers

• Bulk Operations with Triggers

Cursors and Memory Limitations

• Cursor declaration and Life cycle

• STATIC

• DYNAMIC

• SCROLL Cursors

• FORWARD_ONLY and LOCAL Cursors

• KEYSET Cursors with Complex SPs

Transactions Management

• ACID Properties and Scope

• EXPLICIT Transaction types

• IMPLICIT Transactions and options

• AUTOCOMMIT Transaction and usage

• SAVEPOINT and Query Blocking

Introduction to Data Science

• What is Analytics & Data Science

• Common Terms in Data Science

• What is data

• Classication of data

• Relevance in industry and need of the hour

• Types of problems and business objectives in various industries

• How leading companies are harnessing the power of analytics

• Critical success drivers.

• Overview of Data Science tools & their popularity.

• Data Science Methodology & problem-solving framework.

• List of steps in Data Science projects

• Identify the most appropriate solution design for the given problem statement

• Project plan for Data Science project & key milestones based on effort estimates

• Build Resource plan for Data Science project

• Why Python for data science

Accessing/Importing and Exporting Data

• Importing Data from various sources (Csv, txt, excel, access etc)

• Database Input (Connecting to database)

• Viewing Data objects - sub setting, methods

• Exporting Data to various formats

• Important python modules Pandas

Data Manipulation Cleansing - Munging Using Python Modules

• Cleansing Data with Python

• Filling missing values using lambda function and concept of Skewness.

• Data Manipulation steps (Sorting, ltering, duplicates, merging, append ing, sub setting, derived variables, sampling, Data type conversions, renaming, formatting.

• Normalizing data

Feature Engineering in Data Science

• Feature Engineering

• Feature Selection

• Feature scaling using Standard Scaler/Min-Max scaler/Robust Scaler.

• Label encoding/one hot encoding

Data Analysis Visualization Using Python

• Introduction exploratory data analysis

• Descriptive statistics, Frequency Tables and summarization

• Univariate Analysis (Distribution of data & Graphical Analysis)

• Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)

• Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ densi ty etc.)

• Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas etc.)

Introduction to Statistics

• Descriptive Statistics

• Sample vs Population Statistics

• Random variables

• Probability distribution functions

• Expected value

• Normal distribution

• Gaussian distribution

• Z-score

• Spread and Dispersion

• Correlation and Co-variance

Introduction to Predictive Modelling

• Concept of model in analytics and how it is used

• Common terminology used in Analytics & Modelling process

• Popular Modelling algorithms

• Types of Business problems - Mapping of Techniques

• Different Phases of Predictive Modelling

EDA (Exploratory Data Analysis)

• Need for structured exploratory data

• EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)

• Identify missing data

• Identify outliers’ data

• Imbalanced Data Techniques

Data Pre-Processing & Data Mining

• Data Preparation

• Feature Engineering

• Feature Scaling

• Datasets

• Dimensionality Reduction

• Anomaly Detection

• Parameter Estimation

• Data and Knowledge

• Selected Applications in Data Mining