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Learn

Data Science with python

To interrogate the data collection & algorithms

Learn

Data Science (with python)

You can create macros to automate repetitive word and

data-processing function and generate

custom, graphs and reports.

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About Courses

The python with data science course covers the basics to advanced python concepts along with in-depth knowledge of data science which includes data analytics, machine learning, data visualization, time series data linear algorithms and different tools available for data science. Data Science with Python has become a required skill for almost every industry. The demand for data science is ever on the rise.

Course Highlights

1.

Data Extraction

2.

Time Series Data

3.

CSV Module

4.

Python Hashing

5.

10+ Assignment

6.

5+ Live Projects

7.

43 Modules

8.

1 Year Free Backup Classes

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Learning Outcome

You will be able to create data structures and do further analysis of the data frame.
You can easily automate your data related to multi-dimensional arrays.
You will be able to visualize the data to identify time series components.
You will be able to store a large number of variables or data using CSV files.
You will be able to use clustering to identify clusters of data objects in a dataset.
You will be able to utilize the backend for URL routing, HTTP responses, accessing databases, and web security..

Software and Apps that you will learn in this course

Course Content

Core Python

1) INTRODUCTION TO LANGUAGES

  1. What is Language?
  2. Types of languages
  3. Introduction to Translators
  4. Compiler
  5. Interpreter
  6. What is Scripting Language?
  7. Types of Script
  8. Programming Languages v/s Scripting Languages
  9. Difference between Scripting and Programming languages
  10. What is programming paradigm?
  11. Procedural programming paradigm
  12. Object Oriented Programming paradigm

2) INTRODUCTION TO PYTHON

  1. What is Python?
  2. Why Python?
  3. Python implementations
  4. Python applications
  5. Python Versions
  6. Python in realtime industry
  7. Difference between Python and 3.x

3) PYTHON SOFTWARES

  1. Python Distributions
  2. Download & Python Installation Process in Windows, Unix, Linux and Mac
  3. Online Python IDLE
  4. Python Real-time IDEs like Spyder, Jupyter Note Book, PyCharm, Rodeo, Visual Studio Code, ATOM, PyDevetc

4) PYTHON LANGUAGE FUNDAMENTALS

  1. Python Implementation Alternatives/Flavors
  2. Keywords
  3. Identifiers
  4. Constants/Literals
  5. Data types
  6. Python VS JAVA
  7. Python Syntax

5) DIFFERENT MODES OF PYTHON

  1. Interactive Mode
  2. Scripting Mode
  3. Programming Elements
  4. Structure of Python program
  5. First Python Application
  6. Comments in Python
  7. Python file extensions
  8. Setting Path in Windows
  9. Edit and Run python program without IDE
  10. Edit and Run python program using IDEs
  11. INSIDE PYTHON
  12. Programmers View of interpreter
  13. Inside INTERPRETER
  14. What is Byte Code in PYTHON?
  15. Python Debugger

6) PYTHON VARIABLES

  1. bytes Data Type
  2. byte array
  3. String Formatting in Python
  4. Math, Random, Secrets Modules
  5. Introduction
  6. Initialization of variables
  7. Local variables
  8. Global variables
  9. 'global' keyword
  10. Input and Output operations
  11. Data conversion functions - int(), float(), complex(), str(), chr(), ord()

7) OPERATORS

  1. Arithmetic Operators
  2. Comparison Operators
  3. Python Assignment Operators
  4. Logical Operators
  5. Bitwise Operators
  6. Shift operators
  7. Membership Operators
  8. Identity Operators
  9. Ternary Operator
  10. Operator precedence
  11. Difference between "is" vs "=="

8) INPUT & OUTPUT OPERATORS

  1. Print
  2. Input
  3. Command-line arguments

9) CONTROL STATEMENTS

  1. Conditional control statements
  2. If
  3. If-else
  4. If-elif-else
  5. Nested-if
  6. Loop control statements
  7. for
  8. while
  9. Nested loops
  10. Branching statements
  11. Break
  12. Continue
  13. Pass
  14. Return
  15. Case studies

10) DATA TYPES AND STRUCTURES IN PYTHON

  1. Introduction
  2. Importance of Data structures
  3. Applications of Data structures
  4. Types of Collections
  5. Sequence
  6. Strings, List, Tuple, range
  7. Non sequence
  8. Set, Frozen set, Dictionary
  9. Strings
  10. What is string
  11. Representation of Strings
  12. Processing elements using indexing
  13. Processing elements using Iterators
  14. Manipulation of String using Indexing and Slicing
  15. String operators
  16. Methods of String object
  17. String Formatting
  18. String functions
  19. Case studies

11) LIST COLLECTION

  1. What is List
  2. Need of List collection
  3. Different ways of creating List
  4. List comprehension
  5. List indices
  6. Processing elements of List through Indexing and Slicing
  7. List object methods
  8. List is Mutable
  9. Python Arrays:
  10. Case studies

12) TUPLE COLLECTION

  1. What is tuple?
  2. Different ways of creating Tuple
  3. Method of Tuple object
  4. Process tuple through Indexing and Slicing
  5. List v/s Tuple
  6. Case studies

13) SET COLLECTION

  1. What is set?
  2. Different ways of creating set
  3. Difference between list and set
  4. Iteration Over Sets
  5. Accessing elements of set
  6. Python Set Methods
  7. Python Set Operations
  8. Union of sets
  9. Case study

14) DICTIONARY COLLECTION

  1. What is dictionary?
  2. Difference between list, set and dictionary
  3. How to create a dictionary?
  4. PYTHON HASHING?
  5. Accessing values of dictionary
  6. Python Dictionary Methods
  7. Reading items from Dictionary
  8. Delete Keys from the dictionary
  9. Sorting the Dictionary
  10. Python Dictionary Functions and methods
  11. Dictionary comprehension

15) FUNCTIONS

  1. What is Function?
  2. Advantages of functions
  3. Syntax and Writing function
  4. Calling or Invoking function
  5. Classification of Functions
  6. No arguments and No return values
  7. With arguments and No return values
  8. With arguments and With return values
  9. No arguments and With return values
  10. Recursion
  11. Python argument type functions :
  12. Default argument functions
  13. Required(Positional) arguments function
  14. Keyword arguments function
  15. Variable arguments functions
  16. 'pass' keyword in functions
  17. Lambda functions/Anonymous functions

Advance Python

16) Python Modules

  1. Importance of modular programming
  2. What is module
  3. Types of Modules - Pre defined, User defined.
  4. User defined modules creation
  5. Functions based modules
  6. Class based modules
  7. Connecting modules
  8. Import module
  9. From... import
  10. Module alias / Renaming module
  11. Built In properties of module

17) Packages

  1. Organizing python project into packages
  2. Types of packages - pre defined, user defined.
  3. Package v/s Folder
  4. py file
  5. Importing package
  6. PIP
  7. Introduction to PIP
  8. Installing PIP
  9. Installing Python packages
  10. Un installing Python packages

18) OOPs

  1. Procedural v/s Object oriented programming
  2. Principles of OOPS - Encapsulation,Abstraction (Data Hiding)
  3. Classes and Objects
  4. How to define class in python
  5. Types of variables - instance variables, class variables.
  6. Types of methods - instance methods, class method, static method Object initialization
  7. 'self' reference variable
  8. Creating object properties using setaltr, getaltr functions
  9. Encapsulation(Data Binding)
  10. What is polymorphism?

19) Exception Handling & Types of Errors

  1. What is Exception?
  2. Why exception handling?
  3. Syntax error v/s Runtime error
  4. Exception codes - AttributeError, Value Error, IndexError, TypeError...
  5. Handling exception - try except block
  6. Try with multi except
  7. Handling multiple exceptions with single except block
  8. Finally block
  9. Try-except-finally
  10. Try with finally
  11. Case study of finally block
  12. Raise keyword
  13. Custom exceptions / User defined exceptions
  14. Need to Custom exceptions
  15. Case studies

20) Regular expressions

  1. Understanding regular expressions
  2. String v/s Regular expression string
  3. "re" module functions
  4. Match()
  5. Search()
  6. Split()
  7. Findall()
  8. Compile()
  9. Sub()
  10. Subn()
  11. Expressions using operators and symbols
  12. Simple character matches
  13. Special characters
  14. Character classes
  15. Mobile number extraction
  16. Mail extraction
  17. Different Mail ID patterns
  18. Data extraction
  19. Password extraction
  20. URL extraction
  21. Vehicle number extraction
  22. Case study

21) File & Directory Handling

  1. Introduction to files
  2. Opening file
  3. File modes
  4. Reading data from file
  5. Writing data into file
  6. Appending data into file
  7. Line count in File
  8. CSV module
  9. Creating CSV file
  10. Reading from CSV file
  11. Writing into CSV file
  12. Object serialization - pickle module
  13. XML parsing
  14. JSON parsing

22) Date & Time module

  1. How to use Date & Date Time class
  2. How to use Time Delta object
  3. Formatting Date and Time
  4. Calendar module
  5. Text calendar
  6. HTML calendar

23) Tkinter & Turtle

  1. Introduction to GUI programming
  2. Tkinter module
  3. Tk class
  4. Components / Widgets
  5. Label, Entry, Button, Combo, Radio
  6. Types of Layouts
  7. Handling events
  8. Widgets properties
  9. Case studies

Data Science

24) Introduction to Data Science and Statistical Analytics

  1. Introduction to Data Science and Statistical Analytics
  2. Introduction to Data Science
  3. Use cases
  4. The need for Business Analytics
  5. Data Science Life Cycle
  6. Different tools available for Data Science

25) Machine learning

  1. Learning Introduction
  2. Learning Applications
  3. Life cycle of Machine Learning
  4. Install Anaconda & Python
  5. Al vs Machine Learning
  6. How to Get Datasets
  7. Data Preprocessing
  8. Supervised Machine Learning
  9. Unsupervised Machine Learning
  10. Supervised vs Unsupervised Learning

26) Advance Excel

  1. Introduction To Spreadsheet Programs
  2. Advance Excel Formulas &Function.
  3. Advance Excel:Features & Techniques.
  4. Numpy
  5. Introduction
  6. Scipy
  7. Introduction
  8. Arrays
  9. Datatypes
  10. Matrices
  11. N dimension arrays
  12. Indexing and Slicing
  13. Pandas
  14. Introduction
  15. Data Frames
  16. Merge, Join, Concat
  17. Mat PlotLib introduction
  18. Drawing plots
  19. Introduction to Machine learning
  20. Types of Machine Learning?
  21. Introduction to Data science

27) PANDAS

  1. Introduction
  2. Environment Setup
  3. Introduction to Data Structure
  4. Series
  5. DataFrame
  6. Panel
  7. Basic Functionality
  8. Descriptive Statistics
  9. Pandas - Function Application
  10. Reindexing
  11. Iteration
  12. Sorting
  13. Working with Text Data
  14. Options and Customization
  15. Indexing and Selecting Data
  16. Statistical Functions
  17. Window Functions
  18. Aggregations
  19. Missing Data
  20. Group By
  21. Merging/Joining
  22. Concatenation
  23. Date Functionality
  24. Time delta
  25. Categorical Data
  26. Visualization
  27. IO Tools
  28. Sparse Data
  29. Caveats & Got chas
  30. Comparison with SQL

28) NUMPY

  1. Introduction
  2. Environment
  3. Ndarray Object
  4. Data Types
  5. Array Attributes
  6. Array Creation Routines
  7. Array From Existing Data
  8. Array From Numerical Ranges
  9. Indexing & Slicing
  10. Advanced Indexing
  11. Broadcasting
  12. Iterating Over Array
  13. Array Manipulation
  14. Binary Operators
  15. String Functions
  16. Mathematical Functions
  17. Arithmetic Operations
  18. Statistical Functions
  19. Sort, Search & Counting Functions
  20. Matrix Library
  21. Linear Algebra

29) MATPLOTLIB

  1. IntroMatplotlib
  2. Matplotlib Plotting
  3. Matplotlib Markers
  4. Matplotlib Line
  5. Matplotlib Labels
  6. Matplotlib Grid
  7. Matplotlib Subplots
  8. Matplotlib Scatter
  9. Matplotlib Bar

30) SCIPY

  1. SciPy Intro
  2. SciPy Sparse Data
  3. SciPy Constants
  4. SciPy Spatial Data
  5. SciPy Optimizers
  6. SciPy Graphs
  7. SciPy Matlab Arrays
  8. SciPy Interpolation

31) SEABORN

  1. Seaborn Introduction
  2. Seaborn Lineplot
  3. Seaborn Histrogram
  4. Seaborn Barplot
  5. Seaborn Scatter Plot
  6. Seaborn Heatmap
  7. Seaborn Pairplot

32) MACHINE LEARNING ALGORITHM

  1. Linear Regrassion algorithm
  2. Ridge and lasso regression
  3. Root mean square
  4. R squared regression analysis
  5. Polynomial regression
  6. Support vector machine
  7. Support vector regression
  8. Support vector machine classification
  9. Decision tree classification
  10. Random forest regression model
  11. Save and load machine model
  12. K nearest neighbor classification algorithm
  13. K nearest neighbor regression algorithm
  14. Naive bayes classifier algorithm

33) DATA EXPLORATION & DATA WRANGLING

  1. Importing and Exporting data from an external source
  2. Data exploratory analysis

34) Introduction to Statistics

  1. Terminologies of Statistics
  2. Measures of Centers, Measures of Spread
  3. Probability
  4. Normal Distribution
  5. Binary Distribution
  6. Hypothesis Testing
  7. Chi-Square Test
  8. ANOVA

35) PREDICTIVE MODELING - 1 (LINEAR REGRESSION)

  1. Supervised Learning - Linear Regression, Bivariate Regression, Multiple Regression Analysis, Correlation (Positive, negative and neutral)
  2. Industrial Case Study
  3. Machine Learning Use-Cases
  4. Machine Learning Process Flow
  5. Machine Learning Categories

37) DECISION TREES

  1. What are Classification and its use cases? > What is Decision Tree?
  2. Algorithm for Decision Tree Induction
  3. Creating a Perfect Decision Tree
  4. Confusion Matrix

38) RANDOM FOREST

  1. Random Forest
  2. What is Naive Bayes?

39) UNSUPERVISED LEARNING

  1. What is Clustering & its Use Cases?
  2. What is K-means Clustering?
  3. What is Canopy Clustering?
  4. What is Hierarchical Clustering?

40) ASSOCIATION ANALYSIS AND RECOMMENDATION ENGINE

  1. Market Basket Analysis (MBA)
  2. Association Rules
  3. Apriori Algorithm for MBA
  4. Introduction of Recommendation Engine
  5. Types of Recommendation - User Based and Item-Based
  6. Recommendation Use-case

41) SENTIMENT ANALYSIS

  1. Introduction to Text Mining
  2. Introduction to Sentiment
  3. Setting up API Bridge, between R and Twitter Account
  4. Extracting Tweet from Twitter Acc
  5. Scoring the tweet

42) TIME SERIES

  1. What is Time Series data?
  2. Time Series variables
  3. Different components of Time Series data
  4. Visualize the data to identify Time Series Components
  5. Implement ARIMA model for forecasting
  6. Exponential smoothing models
  7. Identifying different time series scenario based on which different Exponential Smoothing model can be applied
  8. Implement respective ETS model for forecasting

Jobs You will Get After Completing Course

Python with data science is an increasingly required skill for many data science positions in each and every industry. The jobs for data science with python have appeared to increase roughly about 55% and have been continuously growing for years. Organizations of all sizes and Industries have increased their demand for the professionals who have advanced skills in python, machine learning and data science and are ready to offer high salaries in return for these skills.

Job profile

After completing this course

Average salary

( 1+ year experience)

Data Scientist 60k- 80k
Data Engineer 50k- 60k
Data Analyst 35k- 45k
Machine Learning Engineer 45k- 60k
Financial Analyst 30k- 45k
Product Analyst 37k- 45k
Data Journalist 35k- 50k

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Frequently Asked Questions

We provide internship and 100% job assistance to those students who are hard-working and well-mannered till the course is completed. We conduct interviews with our recruiters for you to get hired that help you to gain professionalism in this field.

Students can pursue this course if they have completed class 10th from a recognised board and are eager to learn programming and coding skills.

With the fast development of modern technology, data science is currently in extremely high demand, the use of Python for data science applications has been gaining steam in recent years. It is useful in web apps and cloud computing platforms and by learning this course you will be able to access different python libraries and frameworks.

Yes we provide 50% scholarship for those students who cannot afford but want to learn about data science and python for building their career.

Yes, IFDA Institute focuses on providing both theoretical as well as practical training to enhance your skills which helps in your career growth.

Yes, we provide weekly classes or you can schedule your class according to your suitable time. We also provide online and backup classes so you can attend your missed or pending lectures.

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