Course Description:
Python with Data Science has been designed by industry experts to help you learn data science concepts and build powerful models to generate useful business insights or predictions. With real-business projects, you will get hands-on experience with real-business problems. Not only that, we will mentor you on your Final Project work and provide useful insights to solve industry level problems in the real-business world. Here, you will learn how data science projects are run, and what are the real-business project challenges that you can face as a Data Scientist / Analyst. Imagine how you would feel when you have the skills to analyse complex business data and you could make sales predictions or recommend the next business opportunity for your organization.
Introduction to CORE Python
- History and Background
- Comparison with other Programming Languages
- Installation and Environment Setup
- Working in IDLE and CMD
- Working in VS-Code
- Working in Jupyter Notebook (Anaconda)
- Working in Google Colaboratory (with Google Drive)
- Writing your First Python Program
Core Programming Fundamentals
- Displaying a Message
- Datatypes and Variables
- String Operations
- Concatenation
- Using title(), upper(), lower(), lstrip(), rstrip(), strip()
- Arithmetic Operations
- Add, Subtract, Multiply, Divide, Modulus
- Using ** and //
- Using Math Functions [import math] : sqrt(), factorial(), pow(), pi
- Conditional Statements with Relational and Logical Operators
- If… Else
- If… Elif… Else
- Nested if… Else
- Loops and Ranges
- While loop
- range() and for loop
- Creating Patterns
- Using break, continue and pass statements
- Basic Data Structures
- List using [ ]
- Using append(), extend(), insert(), remove(), pop(), clear(), index(), count(), sort(), reverse(), copy(), len(), max(), min(), range()
- Slicing a List using [_:_:_] notation
- Tuples using ( )
- Dictionary using { key : value }
- Using clear(), copy(), fromkeys(), get(), items(), keys(), popitem(), setdefault(), pop(), len(), del
- Set using { }
- Using add(), remove(), discard(), union(), intersection(), isdisjoint(), difference()
Comprehension
- List Comprehension
- Dictionary Comprehension
- Set Comprehension
Working with Strings
- String representation and structure
- String input and output
- String Slicing
- String Functions
- Using len(), str(), upper(), lower(), format(), find(), replace()
- Using in and not in operators
Functions
- Definition and Types of Function
- Working with Global and Local Variables
- Anonymous Function
- Lambda
- Filter, map, reduce
- Recursive Function
- Using *args and **kwargs
- Generators
Modules and Packages
- Introduction to Modules
- Importing a module
- Working with math and os modules
- Working with datetime module
- Using now(), today()
- Introduction to Packages
- Working with packages
Exception Handling
- Definition and Example of Exception
- Using try and except block
- Using raise keyword
- Using finally block
- Using try with else block
File Handling
- Definition
- Opening and Closing a File
- Writing and Reading a File
- File Methods
Object Oriented Programming
- Definition
- Classes and Objects
- Encapsulation
- Inheritance
- Polymorphism
Regular expressions
- Introduction to CFG
- Match Function
- Search Function
- Matching VS Searching
- Patterns
Introduction to Data Science
- What is analytics and data science?
- Overview of data science and analytics.
- Why analytics is becoming popular now?
- Application of analytics in business.
- Analytics vs data warehousing and MIS reporting.
- Various terminologies in analytics.
- How business are using the power of analytics?
- Various analytics tools and their usage.
Introduction to Data Science with Python
- Installing python anaconda distribution.
- Python native data types.
- Basic programming concepts.
- Python data science packages overview.
Python Basics: Basics Syntax, Data Structures
- Python objects
- Math and comparison operators
- Conditional statements
- Loops
- Functions
- Exception handling
Python Concepts (Core)
- Overview and history of python.
- Python installation.
- Introduction to python editors & IDE’S (canaopy, pycharm, jupyter, rodeo etc.)
- Understand jupyter notebook & customize settings.
- Concept of packages / libraries – important packages (numpy, scipy, scikit-learn, pandas, matplotlib etc).
- Installing & loading packages & name spaces.
- Data types & data objects/ structure (strings, tuples, lists, dictionaries).
- List & dictionary comprehensions.
- Variables & value labels – date, times & values.
- Basic operations – mathematical – string – date.
- Reading & writing data.
- Simple plotting.
- Control flow & conditional statements.
- Debugging & code profiling.
- How to create class & modules and how to call them ?
Numpy Package
- What is numpy?
- Importing numpy
- Numpy overview
- Numpy array creation & basic operation
- Numpy universal function.
- Selecting & retrieving data.
- Data slicing
- Iterating numpy data.
- Shape manipulation.
- Stacking & splitting arrays.
- Copies and views : no copy, shallow copy, deep copy.
- Indexing : arrays of indices, Boolean arrays.
Introduction to Pandas
- Selecting data from pandas data frame.
- Slicing & dicing using pandas.
- Group by/ aggregate.
- Strings with pandas.
- Cleaning up messy data with pandas.
- Dropping entries.
- Selecting entries
Data Manipulation using Pandas
- Data alignment
- Sorting and ranking
- Summary statistics
- Missing values
- Merging data
- Concatenation
- Combining DataFrames.
- Pivot
- Duplicates
- Binning
Pandas Package
- Importing pandas
- Pandas overview
- Object creation: series object, data frame object
- View data
- Selecting data by label & position
- Data slicing
- Boolean indexing
- Setting data
Python Advance : Data Mugging with Pandas
- Applying functions data
- Histogramming
- String methods
- Merge data: concat, join & append
- Grouping & aggregation
- Reshaping.
- Analyzing data for missing values.
- Filling missing values : fill with constant, forward filling, mean.
- Removing duplicates
- Transforming data
Python Advance: Visualization with Matplotlib
- Anatomy of a matplotlib plot.
- Matplotlib basics plots and it’s containers.
- A matplotlib figure, it’s components and properties.
- Axes and other graphical objects.
- Pylab and pyplot.
- Data and matplotlib plots.
- What is subplot?
- Modifying size of figures.
- Plotting size of figures.
- Plotting routines with pyplot.
- Customizing your pyplot.
- Deleting an axes.
- Setting up plot title, axes labels, legend, layout.
- Showing, saving and closing your plot.
- Save a plot to an image file and pdf file.
- Use Cla (). Clf () or close
Statistics for Data Science
Introduction to statistics
- Two areas of statistics in data science.
- Applies statistics in business.
- Descriptive statistics.
- Inferential statistics.
- Statistics terms and definitions.
- Type of data
- Quantitative vs qualitative data
- Data measurement scales.
Harnessing Data
- Sampling data, with and without replacement.
- Sampling methods, random vs non – random
- Measurement on samples.
- Random sampling methods.
- Simple random, stratified, cluster, systematic sampling.
- Biased vs unbiased sampling.
- Sampling error
- Data collection methods.
Exploratory Analysis
- Measures of central tendencies.
- Mean, median and mode
- Data variability : range, quartiles, standard deviation
- Calculating standard deviation.
- Z-score/ standard score
- Empirical rule
- Calculating percentiles.
- Outliners
Distributions
- Distribution introduction
- Normal distribution
- Central limit theorem
- Histogram – normalization
- Other distribution : poisson, binominal etc.
- Normality testing
- Skewness
- Kurtosis
- Measure of distance
- Euclidean, manhattan and minkowski distance.
Hypothesis and computational techniques
- Hypothesis testing
- Null hypothesis, p- value
- Need for hypothesis testing in business.
- Two tailed, left tailed and right tailed test.
- Hypothesis testing outcomes : type 1 &2 errors.
- Parametric test, t – tests : one sample, two sample, paired.
- One way ANOVA.
- Importance of parametric tests
- Non parametric tests : chi- square, mann – whitney, Kruskal- wallis etc.
- Which test to choose?
- Ascertaining accuracy of data
Correlation and Regression
- Introduction to regression
- Types of regression
- Hands on of a regression with python
- Correlation
- Weak and strong correlation
- Finding correlation with python .
Python Training center in West Delhi, Uttam nagar. Best certified computer course curriculum for professional learning institute near Dwarka, Uttam Nagar, Jankapuri, Tilak Nagar, Subhash Nagar, Tagore Garden, Rajouri Garden, Ramesh Nagar, Moti Nagar, Kirti Nagar, Shadipur, Patel Nagar, Rajendra Place, Karol Bagh, Jhandewalan, Ramakrishna Ashram, Connaught Place, vikaspuri, najafgarh, Delhi Cantt, Dhaula Kuan, Palam Colony, Palam Villag, Dabri, Hari Nagar, Fateh Nagar, Raja Garden, Punjabi Bagh, Shivaji Park, Paschim Vihar, Peeragarhi, Inderlok, Netaji Subhash Place, Pitampura, Rohini, Chandni Chowk, New Delhi, East Delhi, North Delhi, West Delhi.