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    Course content

    About Python With Data Analytics Training Program

  • Introduction to the Course
  • macOS - Download and Install the Anaconda Distribution
  • Windows - Download and Install the Anaconda Distribution
  • How to Uninstall the Anaconda Distribution
  • Use Anaconda Navigator to Create a New Environment
  • Download Course Materials
  • Unpack Course Materials + The Startdown and Shutdown Process
  • Intro to the Jupyter Lab Interface
  • Code Cell Execution
  • Import Libraries into Jupyter Lab
  • Installation and Setup

  • Comments
  • Basic Data Types
  • Operators
  • Variables
  • Declare Variables
  • Built-in Functions
  • Custom Functions
  • String Methods
  • Lists
  • Creating Lists
  • Index Positions and Slicing
  • Dictionaries
  • Creating Dictionaries
  • Classes
  • Navigating Libraries using Jupyter Lab
  • Python Crash Course

  • Create a Series Object from a List
  • Create a Series Object from a Dictionary
  • Intro to Series Methods
  • Intro to Attributes
  • Attributes and Methods on a Series
  • Parameters and Arguments
  • Import Series with the pd.read_csv Function
  • The head and tail Methods
  • Passing Series to Python Built-In Functions
  • Check for Inclusion with Python's in Keyword
  • The sort_values Method
  • The sort_index Method
  • Extract Series Values by Index Position
  • Extract Series Values by Index Label
  • The get Method
  • Overwrite a Series Value
  • The copy Method
  • Math Methods on Series Objects
  • Broadcasting
  • The value_counts Method
  • The apply Method
  • The map Method
  • Series

  • Methods and Attributes between Series and DataFrames
  • Select One Column from a DataFrame
  • Select Multiple Columns from a DataFrame
  • Add New Column to DataFrame
  • A Review of the value_counts Method
  • Drop DataFrame Rows with Missing Values
  • Fill in Missing Values with the fillna Method
  • The astype Method 
  • Sort a DataFrame with the sort_values Method 
  • Sort DataFrame with the sort_index Method
  • Rank Series Values with the rank Method
  • DataFrames

  • This Module's Dataset + Memory Optimization
  • Filter a DataFrame Based on a Condition
  • Filter with More than One Condition (AND - &)
  • Filter with More than One Condition (OR - |)
  • The isin Method
  • The isnull and notnull Methods
  • The between Method
  • The duplicated Method
  • The drop_duplicates Method
  • The unique and nunique Methods

  • This Module's Dataset
  • The set_index and reset_index Methods
  • Retrieve Rows by Index Position with iloc Accessor
  • Retrieve Rows by Index Label with loc Accessor
  • Second Arguments to loc and iloc Accessors
  • Overwrite Value in a DataFrame
  • Overwrite Multiple Values in a DataFrame
  • Rename Index Labels or Columns in a DataFrame
  • Delete Rows or Columns from a DataFrame
  • Create Random Sample with the sample Method
  • The nsmallest and nlargest Methods
  • Filtering with the where Method
  • The apply Method with DataFrames

  • This Module's Dataset
  • Common String Methods
  • Filtering with String Methods
  • String Methods on Index and Columns
  • The split Method
  • More Practice with Splits
  • The expand and n Parameters of the split Method

  • Intro to the MultiIndex Module
  • Create a MultiIndex
  • Extract Index Level Values
  • Rename Index Labels
  • The sort_index Method on a MultiIndex DataFrame
  • Extract Rows from a MultiIndex DataFrame
  • The transpose Method
  • The stack Method
  • The unstack Method
  • The pivot Method
  • The melt Method
  • The pivot_table Method

  • Intro to the GroupBy Module
  • The groupby Method
  • Retrieve A Group with the get_group Method
  • Methods on the GroupBy Object
  • Grouping by Multiple Columns
  • The agg Method
  • Iterating through Groups

  • Intro to the Merging DataFrames Module
  • The pd.concat Function I
  • The pd.concat Function II
  • Left Joins
  • The left_on and right_on Parameters
  • Inner Joins I
  • Inner Joins II
  • Full-Outer Joins
  • Merging by Indexes with the left_index and right_index Parameters
  • The join Method

  • Intro to the Working with Dates and Times Module and Review of Python's datetime
  • The Timestamp and DatetimeIndex Objects
  • Create Range of Dates with pd.date_range Function
  • The dt Attribute
  • Selecting Rows from a DataFrame with DatetimeIndex
  • The DateOffset Object
  • Specialized Date Offsets
  • Timedeltas

  • URL for Next Lesson's Dataset
  • Intro to the Input and Output Module
  • Export DataFrame to CSV File
  • Install openpyxl Library to Read and Write Excel Files
  • Import Excel File into pandas
  • Export Excel File from pandas

  • Install matplotlib Library for Visualization
  • The plot Method
  • Modifying Plot Aesthetics with Templates
  • Bar Charts
  • Pie Charts

  • Introduction to the Options and Settings Module
  • Changing Options with Attributes
  • Changing Options with Functions
  • The precision Option

  • Overview of BI Concepts
  • Why We Need BI
  • Introduction to SSBI
  • SSBI Tools
  • Why Power BI?
  • What is Power BI?
  • Building Blocks of Power BI
  • Getting Started with Power BI Desktop
  • Get Power BI Tools
  • Introduction to Tools and Terminology
  • Dashboard in Minutes
  • Refreshing Power BI Service Data
  • Interacting with Your Dashboards
  • Sharing Dashboards and Reports

  • How to Use Visuals in Power BI
  • What Are Custom Visuals?
  • Creating Visualizations and Color Formatting
  • Setting Sort Order
  • Scatter & Bubble Charts & Play Axis
  • Tooltips and Slicers, Timeline Slicers & Sync Slicers
  • Cross Filtering and Highlighting
  • Visual, Page, and Report Level Filters
  • Drill Down/Up
  • Hierarchies and Reference/Constant Lines
  • Tables, Matrices & Conditional Formatting
  • KPIs, Cards & Gauges
  • Map Visualizations
  • Managing and Arranging Visuals
  • Drill Through and Custom Report Themes

  • What Is Data Visualization?
  • Comparison and Benefits Against Reading Raw Numbers
  • Real Use Cases from Various Business Domains
  • Quick and Powerful Examples Using Tableau (Without Technical Details)
  • Installing Tableau
  • Tableau Interface
  • Connecting to Data Source
  • Tableau Data Types
  • Data Preparation

  • Installation of Tableau Desktop
  • Architecture of Tableau
  • Interface of Tableau
  • Layout
  • Toolbars
  • Data Pane
  • Analytics Pane
  • How to Start with Tableau
  • Ways to Share and Export Work Done in Tableau

  • Connection to Excel
  • Connection to Cubes and PDFs
  • Management of Metadata and Extracts
  • Data Preparation
  • Joins
  • Union
  • Dealing with NULL Values
  • Cross-Database Joining
  • Data Extraction
  • Data Blending

  • Filters
  • Interactive Filters
  • Creating Folders in Tableau
  • Sorting in Tableau
  • Types of Filters
  • Filtering the Order of Operations

  • Correct Execution of the Project
  • Scope Control
  • WBS Creation
  • Requirements Collection

  • Monitoring the Risks
  • Performing Risk Analysis
  • Planning the Risk Responses
  • Identifying the Risks
  • Implementing the Risk Responses

  • Creating a Project Team
  • Project Governance
  • Influence of an Organization on Project Management
  • Project Life Cycle
  • Project Stakeholders
  • Importance of the Project Team

  • Project Scope Management
  • Project Cost Management
  • Project Resource Management
  • Project Risk Management
  • Project Stakeholder Management
  • Project Integration Management
  • Project Schedule Management
  • Project Quality Management
  • Project Procurement Management

  • Introduction to SAS Software
  • Industries Using SAS
  • Components of SAS System
  • Architecture of SAS System
  • Functionality of SAS System
  • Introduction to SAS Windows

  • Module 1: Working in the SAS Environment
  • Module 2: Creation of Database from Raw Data
  • Module 3: Output Delivery System
  • Module 4: Combining Datasets
  • Module 5: Functions
  • Module 6: Loops in SAS: Do Loops
  • Module 7: Array
  • Module 8: Procedures
  • Module 9: Graphs
  • Module 10: Proc SQL
  • Module 11: Macro
    • Why Should You Learn Python Data Analytics Training?

    The annual salary of an Python Data Analytics is $125k.

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