Applied Data Science with Python - eLearning

Applied Data Science with Python - eLearning

Learn the increasingly popular programming language for development

COURSE OVERVIEW

Python is a general-purpose programming language that is growing in popularity. Companies around the world are using Python to learn from data and gain an edge over their competitors. Unlike any other Python course, this program focuses on Python specifically for data science. You will learn how to store and manipulate data as well as useful tools to start your own analyses.

The Python for Data Science course covers the fundamental concepts of Python programming and explains data analysis, machine learning, data visualization, web scraping and natural language processing. You will gain a comprehensive understanding of the different packages and libraries needed to perform aspects of data analysis.

WHAT IS INCLUDED?

• Course and material are in english
• Beginner - intermediate level
• 1 year access to the self-paced study eLearning platform 24/7
• 6 hours of video content
• 40 hours study time recommended
• Virtual labs, Test simulation, End-Projects
• No exam for the course but student will get certification of training completion
• BONUS FREE COURSE: Statistics essentials for data science
• Bonus 36 hours Live Online Class! 

Bonus: In addition to this hands-on e-learning course, we offer you free access to our online classroom sessions whenever its available (every 2-3 months) in addition to your e-learning if you wish. You will have the opportunity to have interaction with the trainer and other participants. These online classroom sessions will also be recorded, so that you can keep the recording for 1 year.

COURSE OBJECTIVES You will learn:

By the end of the course, you will be able to:

• Acquire an in-depth understanding of the processes of data science, data exploration, data visualization, hypothesis development and testing.
• Install the necessary Python environment and other auxiliary tools and libraries
• Understand the basic concepts of Python programming such as data types, taps, lists, dict, basic operators and functions.
• Perform high-level mathematical calculations with the NumPy package and its large library of mathematical functions
• Perform scientific and engineering calculations with the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
• Perform data analysis and manipulation using the data structures and tools provided in the Pandas package
• Acquire expertise in machine learning with the Scikit-Learn package
• Understand supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipelining.
• Use the Scikit-Learn package for natural language processing
• Use the matplotlib library in Python for data visualization
• Extract useful information from websites by web scraping with Python
• Integrate Python with Hadoop, Spark and MapReduce

Who Should Enroll in this Program?

This course is ideal for individuals who are interested in pursuing a career in data science, machine learning, or artificial intelligence, and are looking to enhance their Python programming and data analysis skills.

• Aspiring Data Scientists
• Data Analysts
• Software Engineers or Programmers
• Researchers and Academics
• Machine Learning Enthusiasts
• Students and Graduates

Prerequisites

Learners need to possess an undergraduate degree or a high school diploma. Additionally, a curiosity for data analysis and a desire to explore the applications of Python in the field of data science is highly encouraged. It is also recommended to have:

• Basic Python Programming Knowledge: Familiarity with basic Python programming concepts such as variables, loops, functions, and control flow.
• Basic Understanding of Statistics: A basic understanding of statistics, including concepts like mean, median, standard deviation, probability, and correlation.
• Mathematics: Basic math skills, particularly in areas like algebra and linear algebra, will be helpful, especially when working with machine learning algorithms or models.

Course content

Introduction to Data Science

• Setting Up Jupyter Notebook
• Python Functions
• Python Types and Sequences
• Python Strings Deep Dive
• Python Demo: Reading and Writing csv files
• Date and Time in Python
• Objects in Python Map
• Lambda and List Comprehension
• Why Python for Data Analysis?
• Python Packages for Data Science
• StatsModels Package
• Scipy Package

Essentials of Python Programming

• Setting Up Jupyter Notebook
• Python Functions
• Python Types and Sequences
• Python Strings Deep Dive
• Python Demo: Reading and Writing csv files
• Date and Time in Python
• Objects in Python Map
• Lambda and List Comprehension
• Why Python for Data Analysis?
• Python Packages for Data Science
• StatsModels Package
• Scipy Package

NumPy

• Fundamentals of NumPy
• Array shapes and axes in NumPy: Part A
• NumPy Array Shapes and Axes: Part B
• Arithmetic Operations
• Conditional Logic
• Common Mathematical and Statistical Functions in Numpy
• Indexing And Slicing
• File Handling

Linear Algebra

• Introduction to Linear Algebra
• Scalars and Vectors
• Dot Product of Two Vectors
• Linear independence of Vectors
• Norm of a Vector
• Matrix operations
• Rank of a Matrix
• Determinant of a matrix and Identity matrix or operator
• Inverse of a matrix and Eigenvalues and Eigenvectors
• Calculus in Linear Algebra

Statistic Fundamentals

• Importance of Statistics with Respect to Data Science
• Common Statistical Terms
• Types of Statistics
• Data Categorization and Types
• Levels of Measurement
• Measures of Central Tendency
• Measures of Dispersion
• Random Variables
• Sets
• Measures of Shape (Skewness & Kurtosis)
• Covariance and Correlation

Probability Distribution

• Probability,its Importance, and Probability Distribution
• Probability Distribution : Binomial Distribution
• Probability Distribution: Poisson Distribution
• Probability Distribution: Normal Distribution
• robability Distribution: Bernoulli Distribution
• Probability Density Function and Mass Function
• Cumulative Distribution Function
• Central Limit Theorem
• Estimation Theory

Advanced Statistics

• Distribution
• Kurtosis Skewness and Student's T-distribution
• Hypothesis Testing and Mechanism
• Hypothesis Testing Outcomes: Type I and II Errors
• Null Hypothesis and Alternate Hypothesis
• Confidence Intervals
• Margins of error
• Comparing and Contrasting T test and Z test
• Bayes Theorem
• Chi Sqare Distribution
• Chi Square Test and Goodness of Fit
• Analysis of Variance or ANOVA
• ANOVA Termonologies
• Partition of Variance using Python
• F - Distribution using Python
• F - Test

Pandas

• Pandas Series
• Querying a Series
• Pandas Dataframes
• Pandas Panel
• Common Functions In Pandas
• Pandas Functions Data Statistical Function, Windows Function
• Pandas Function Data and Timedelta
• Categorical Data
• Working with Text Data
• Iteration
• Sorting
• Plotting with Pandas

Data Analysis

• Understanding Data
• Types of Data Structured Unstructured Messy etc
• Working with Data Choosing appropriate tools, Data collection, Data wrangling
• Importing and Exporting Data in Python
• Regular Expressions in Python
• Manipulating text with Regular Expressions
• Accessing databases in Python

Data Wrangling

• Pandorable or Idiomatic Pandas Code
• Loading Indexing and Reindexing
• Merging
• Memory Optimization in Python
• Data Pre Processing: Data Loading and Dropping Null Values
• Data Pre-processing Filling Null Values
• Data Binning Formatting and Normalization
• Data Binning Standardization
• Describing Data

Data Visualization

• Principles of information visualization
• Visualizing Data using Pivot Tables
• Data Visualization Libraries in Python Matplotlib
• Graph Types
• Data Visualization Libraries in Python Seaborn, Ploty, Bokeh
• Using Matplotlib to Plot Graphs
• Plotting 3D Graphs for Multiple Columns using Matplotlib
• Using Matplotlib with other python packages
• Using Seaborn to Plot Graphs
• Plotting 3D Graphs for Multiple Columns Using Seaborn
• Introduction to Plotly and Bokeh

BONUS FREE COURSE: Statistics essentials for data science

• Introduction to Statistics
• Understanding the Data
• Descriptive statistics
• Data visualization
• Probability
• Probability distributions
• Sampling and sampling techniques
• Inferential statistics
• Application of inferential statistics
• Relation between variables
• Application of statistics in Business
• Assisted Practice

Course Project

The course also includes real-world, industry-based projects. Successful evaluation of one of the following projects is part of the eligibility criteria:

Project 1: Sales Analysis for Business Growth

Analyze the sales data of a retail clothing company and support the management in formulating their sales and growth strategy.

Project 2: Marketing Campaign Analysis

Perform exploratory data analysis and hypothesis testing to better understand the various factors contributing to customer acquisition.

Project 3: Real Estate Data Visualization

Analyze the housing dataset using various types of plots to gain insights into the data.

Project 4: Housing Price Analysis

Analyze housing data to uncover insights into house prices, comprehend the elements influencing house prices, and understand the impact of various house features on their price.

Project 5: Customer Behaviour Analysis

Utilize various probability distributions to analyze customer behaviors and store performance metrics using a custom dataset.