Best Data Analytics Training in Noida

MODULE 3:DATA HANDLING

• Data Types
• Numbers
• Strings
• Lists
• Tuples
• Dictionary
• Set
• Frozen set
• Bool
• Mutable and Immutable

MODULE 4:STRING MANIPULATION

• Introduction to Python String
• Accessing Individual Elements
• String Operators
• String Slices
• String Functions and Methods

MODULE 5:LIST MANIPULATION

• Introduction to Python List
• Creating List
• Accessing List
• Joining List
• Replicating List
• List Slicing

MODULE 6:TUPLES

• Introduction to Tuple
• Creating Tuples
• Accessing Tuples
• Joining Tuples
• Replication Tuples
• Tuple Slicing

MODULE 7:DICTIONARIES

• Introduction to Dictionary
• Accessing values in dictionaries
• Working with dictionaries
• Properties

MODULE 8:SET AND FROZENSET

• Introduction to Set and Frozen set
• Creating Set and Frozen set
• Accessing and Joining
• Replicating and Slicing

MODULE 9:OPERATORS

• Arithmetic Operators
• Relational Operators
• Logical Operators
• Membership Operators
• Identity Operators
• Bitwise Operators
• Assignment Operators
• Operators Precedence
• Evaluating Expression
• Type Casting

MODULE 10:PROGRAM CONTROL FLOW

• Conditional Statements
• The if Statement
• The if-else Statement
• The if-elif Statement
• Nested if Statements
• Python Indentation
• Looping and Iteration
• The For Loop
• The While Loop
• Loop else Statement
• Nested Loops
• Break and Continue
• The Range Function
• Introduction to range()
• Types of range() function Use of range() function

MODULE 11:INTRODUCTION TO FUNCTIONS BUILT-IN FUNCTIONS

• Introduction to Functions
• Using a Functions
• Python Function Types
• Structure of Python Functions
• E.g. - map, zip, reduce, filter, any, chr, ord, sorted, globals, locals, all, etc.

• Structure of a Python Program w.r.t. UDF
• Types of Functions
• Invoking UDF
• Flow of Execution
• Arguments and Parameters
• Default Arguments, Named Arguments
• Scope of Variables
• Lambda function

MODULE 12:RECURSION FUNCTION

• Use of recursion function

MODULE 13:MODULES AND PACKAGES BUILT-IN MODULES

• Importing Modules in Python Programs
• Working with Random Modules
• E.g. built ins, OS, time, datetime, calendar, Twilio , smtp, pillow

• Revisiting Python
• List and dictionary
• comprehension
• Programming assignment

MODULE 2:INTRODUCTION TO DATA ANALYTICS

• Why Analytics?
• Traditional Data Management
• Types of Analytics
• Dimensions and measures
• Why learn Python for data analysis?

MODULE 3:LIBRARIES FOR DATA ANALYTICS

• Anaconda
• NumPy
• SciPy
• Pandas
• Matplotlib
• Seaborn

MODULE 4:STATISTICS

• Mean, Median, Mode Z-scores
• Bias -variance dichotomy
• Sampling and t-tests
• Sample vs Population statistics
• Random Variables
• Probability distribution function
• Expected value
• Binomial Distributions
• Normal Distributions
• Central limit Theorem
• Hypothesis testing
• Z-Stats vs T-stats
• Type 1 type 2 error
• Chi Square test
• ANOVA test and F-stats

MODULE 5:JUPYTER NOTEBOOK

• Create Documentation
• Code Mode
• Markdown Mode
• Heading Mode

MODULE 6:NUMPY

• Creating NumPy arrays
• Indexing and slicing in NumPy
• Downloading and parsing data
• Creating multidimensional arrays
• NumPy Data types
• Array tributes
• Indexing and Slicing
• Creating array views copies
• Manipulating array shapes 1/0

MODULE 7:SCIPY

• Introduction to SciPy
• Create function
• modules of SciPy

MODULE 8:PANDAS

• Using multilevel series
• Series and Data Frames
• Grouping, aggregating
• Merge Data Frames
• Generate summary tables
• Group data into logical pieces
• Manipulate dates
• Creating metrics for analysis
• Data wrangling
• Merging and joining
• Analytics Vidhya dataset- Loan Prediction Problem
• Data Mugging using Pandas
• Building a Predictive Model

MODULE 9:MATPLOTLIB

• Scatter plot
• Bar charts, histogram
• Stack charts
• Legend title Style
• Figures and subplots
• Plotting function in pandas
• Labelling and arranging figures
• Save plots

MODULE 10:SEABORN

• Style functions, Colour palettes
• Distribution and Categorical plots
• Regression plots
• Axis grid objects

MODULE 11:WEB SCRAPING

• Scraping Webpages
• Beautiful soup package
• Real time project

MODULE 12:INTRODUCTION TO ML

• What is ML? And Why ML?
• Introduction to Supervised ML
• Introduction to Unsupervised ML
• Mathematical Background for ML
• Matrix ops Probability Theory (Bayes' Theorem) ML Glossary- Variable types, k-fold
• CV, AUC, F1 score, Overfitting/Underfitting
• Data split & hyper parameter

• Real time projects

NOTE: PREREQUISITES:

• Python Programming

• Classroom Training
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• Corporate Training
• On campus Training