NodeBook Private Limited

Data Analyst – Level1

Applied Data Science with Machine Learning

  • Bring your own laptop.
  • No prior knowledge is required. Learning will start from scratch.

Note: Training is completely practical on real world industrial datasets to achieve mastery for an international market.

In this training, you’ll be hands-on on how to master mandatory data scientist technical skills, including
object-oriented and functional approaches with Python, and libraries like scikit-learn, Matplotlib,
NumPy, and pandas. You’ll also master web scraping and database queries, deep learning and machine
learning, and predictive analysis.

To help you stand out from others, we included concepts such as Git, and GitHub to develop efficient
collaboration. Best of all, you’ll learn by doing and apply your skills to several projects involving
realistic business scenarios to build your portfolio and prepare for the international market.

  • Foreign and Local Experienced Trainers;
  • Hands-on Training with Real Working Environment;
  • Internship Opportunities; Affordable Cost;
  • Continuous help to the Participants even after Training sessions via whatsapp groups;
  • Guided projects, Profile building and Specific Resume designing are also done usually during gap between two levels.

As Data Science & Data Literacy are going to be mandatory and regulatory requirements for all domains and industries, any professional belonging to Finance, HR & Admin, Audit, Supply Chain, Engineering, Computer Science, Health Care, etc. are eligible for this training.

No prior knowledge is required. Learning will start from scratch.

Python Programming:

  • Introduction to Python
  • Programming in Python
  • The print() Function
  • Python Syntax
  • Computer Programs
  • Code Comments
  • Arithmetical Operations

Python Variables:

  • Saving Values
  • Variable Names
  • Updating Variables
  • Syntax Shortcuts

Python Data Types:

  • Integers and Floats
  • Conversion Between Types
  • Strings
  • Escaping Special Characters
  • String Concatenation
  • String Conversion
  • Multi-line Strings

Python Lists:

  • Storing Row Elements into Variables
  • Storing Rows as Lists
  • List Length
  • List Indexing
  • Retrieving Values from Lists
  • Negative Indexing
  • Retrieving Multiple List Elements
  • List Slicing
  • List of Lists
  • Retrieving from List of Lists

Python For Loops:

  • Repetitive Processes
  • For Loops
  • For Loop Structure
  • Opening a File
  • Kinds of Errors
  • Dealing with Errors
  • Method to Calculate an Average and its alternate

Python Conditional Statements:

  • If Statements
  • Booleans
  • If Statement Fundamentals
  • Multiple Conditions
  • The OR Operator
  • Combining Logical Operators
  • Comparison Operators
  • Comparison Operator Applications
  • The else Clause
  • The elif Clause
  • Else vs. elif
  • Python Dictionaries:
  • Dictionaries
  • Creating a Dictionary and its alternate
  • Key-Value Pairs
  • Checking for Membership
  • Updating Dictionary Values
  • Counting with Dictionaries
  • Finding the Unique Values
  • Proportions and Percentages
  • Looping over Dictionaries
  • Frequency Tables for Numerical Columns
  • Filtering for the Intervals

Python Functions:

  • Functions
  • Built-In Functions
  • Customized Functions
  • The Structure of a Function
  • Parameters and Arguments
  • The Return Statement
  • Reusability and Multiple Parameters
  • Keyword and Positional Arguments
  • Combining Functions
  • Debugging Functions
  • Interfering with the Built-In Functions
  • Variable Names and Built-In Functions
  • Default Arguments
  • Multiple Return Statements
  • Returning Multiple Variables
  • Tuples
  • No Return Statement
  • Function Behavior
  • Scopes — Global and Local
  • Scopes — Searching Order

List Comprehensions and Lambda Functions:

  • The JSON Format
  • Reading a JSON file
  • Deleting Keys
  • List Comprehensions to Transform and Create Lists
  • List Comprehensions to Reduce a List
  • Functions as Arguments
  • Lambda Functions
  • Lambda Functions to Analyze JSON data
  • JSON files into pandas
  • Exploring Tags Using the Apply Function
  • Extracting Tags Using Apply with a Lambda Function

Cleaning, Preparing and Data Analysis:

  • Introducing Data Cleaning
  • Replacing Substrings with the Replace Method
  • Cleaning Columns
  • String Capitalization
  • Errors During Data Cleaning
  • Parsing Numbers from Complex Strings
  • Summarizing Data
  • Inserting Variables into Strings
  • Creating a Summary Function
  • Formatting Numbers Inside Strings

Python Object-Oriented:

  • Introduction
  • Classes and Objects
  • Defining a Class
  • Instantiating a Class
  • Creating Methods
  • Understanding “self’”
  • Creating a Method that Accepts an Argument
  • Attributes and the Init Method
  • Creating Other Method
  • Creating and Updating an Attribute

 Python Dates and Times:

  • Importing Modules
  • The Datetime Module
  • The Datetime Class
  • Strptime to Parse Strings as Dates
  • Strftime to Format Dates
  • The Time Class
  • Comparing Time Objects
  • Calculations with Dates and Times

Introduction to Pandas:

  • Introduction to Series
  • Introducing DataFrames
  • Series vs DataFrames
  • Selecting a Column from a DataFrame by Label
  • Selecting Rows from a DataFrame by Label
  • Value Counts Method
  • Selecting Items from a Series by Label

Data Exploration with Pandas:

  • Vectorized Operations
  • Series Data Exploration Methods
  • Series Describe Method
  • Method Chaining
  • Dataframe Exploration Methods
  • Dataframe Describe Method
  • Assignment with Pandas
  • Boolean Indexing with Pandas Objects
  • Boolean Arrays to Assign Values
  • Creating New Columns
  • iloc to select by integer position
  • Pandas methods to create boolean masks
  • Working with Integer Labels
  • Pandas Index Alignment
  • Pandas Boolean Operators
  • Sorting Values
  • Loops with Pandas

Line Graphs and Time Series:

  • Matplotlib
  • Customizing a Graph
  • Types of Growth Types of Change
  • Comparing Line Graphs

Scatter Plots and Correlations:

  • Seasonal Trends
  • Scatter Plots
  • Correlation
  • Pearson Correlation Coefficient
  • Measuring Pearson’s r
  • Correlation vs. Causation

Bar Plots, Histograms, and Distributions:

  • Bar Plots and its customization
  • Frequency Tables
  • Grouped Frequency Tables
  • Histograms
  • The Normal Distribution
  • The Uniform Distribution
  • Skewed Distributions

Grid Charts:

  • Pandas Visualization Methods
  • Comparing Graphs
  • Grid Charts and Subplots

Relational Plots:

  • Seaborn
  • Variable Representation;
  • Color
  • Size
  • Shape
  • Spatial Separation

Audience Design:

  • The Familiarity Principle
  • Matplotlib Interfaces
  • The OO Interface
  • Maximizing Data-Ink
  • Erasing Non-Data Ink
  • Erasing Redundant Data-Ink
  • Title and Subtitle
  • Reshaping Data with the Melt Function


  • Data Stories
  • Grid Charts in Matplotlib
  • Faster Workflow
  • Modifications
  • Structural Elements
  • Progress Bar

Gestalt and Attentive Attributes:

  • Gestalt Principles
  • Proximity
  • Similarity
  • Enclosure
  • Connection
  • Visual Hierarchy
  • Pre-Attentive Attributes


  • Matplotlib Styles
  • FiveThirtyEight Style
  • Labels
  • Signature
  • Coloring

Data Aggregation:

  • Loops to Aggregate Data
  • GroupBy Operation
  • GroupBy Objects
  • Aggregation Methods with Groupby
  • Agg() Method
  • Multiple and Custom Aggregations
  • Aggregation with Pivot Tables

Data Combining:

  • Data Concatenation
  • Combining Dataframes with Different Shapes
  • Joining Dataframes with the Merge
  • Left Joins
  • Join on Index

Data Transformation:

  • Function Element-wise Using the Map and Apply Methods
  • Function Element-wise to Multiple Columns Using Applymap Method

Working with Strings:

  • Apply to Transform Strings
  • Vectorized String Methods
  • Exploring Missing Values with Vectorized String Methods
  • Finding Specific Words in Strings
  • Extracting Substrings from a Series
  • Extracting All Matches of a Pattern from a Series
  • Extracting More Than One Group of Patterns from a Series

Missing And Duplicate Data:

  • Identifying Missing Values
  • Correcting Data Cleaning Errors
  • Visualizing Missing Data
  • Using Data From Additional Sources
  • Identifying Duplicates Values
  • Correcting Duplicates Values
  • Handle Missing Values
  • Analyzing Missing Data
  • Imputation

Regular Expression:

  • The Regular Expression Module
  • Matches with pandas Methods
  • Regular Expressions to Select Data
  • Quantifiers
  • Character Classes
  • Matching Text with Capture Groups
  • Negative Character Classes
  • Word Boundaries
  • Matching at the Start and End of Strings
  • Flags to Modify Regex Patterns
  • Capture Groups to Extract Data
  • Lookarounds to Control Matches
  • BackReferences
  • Substituting Regular Expression
  • Multiple Capture Groups
  • Named Capture Groups to Extract Data


  • Introduction to Version Control Systems
  • The git Folder
  • Creating Files in the Repository
  • Checking File Status
  • Configuring Identity in Git
  • Committing Changes
  • Viewing File Differences
  • Making a Second Commit
  • Reviewing the Commit History
  • Viewing Commit Differences

Git Remotes:

  • Introduction to Remote Repositories
  • Making Changes to Cloned Repositories
  • Overview of the Master Branch
  • Pushing Changes to the Remote
  • Viewing Individual Commits
  • Commits and the Working Directory
  • Switching to a Specific Commit
  • Pulling From a Remote Repo
  • Referring to the Most Recent Commit
  • Git Branches:
  • Introduction to Git Branches
  • Switching Branches
  • Pushing a Branch to a Remote
  • Merging Branches
  • Deleting Branches
  • Checking Out Branches From the Remote
  • Finding Differences Across Branches
  • Branch Naming Conventions
  • Branch History

Merge Conflicts:

  • Introduction
  • Aborting a Merge
  • Resolving Conflicts
  • Resolving Multi-Line Conflicts
  • Resolving Multiple Conflicts
  • Accepting Changes From Branch
  • Ignoring Files
  • Removing Cached Files


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