Download Data Science With Python

What you would learn in Data Science With Python course?

"Data Science" course "Data Science" course is an intermediate-level course specifically designed for beginners and professionals.

It covers fundamentals and advanced concepts. The course includes content-based videos and practical demonstrations, which demonstrate and explains each step for completing the task.

Learning Goals:

When you finish the course, you'll be able to master:

• Data Science in depth

• Sectors that are Using Data Science

• Contents and Purpose of Python

• Data Analytics Process

• Exploratory Data Analysis (EDA)

• EDA-Quantitative Technique

• EDA - Graphical Technique

• The Data Analytics conclusion or predictions

• Data Analytics Communication

• Data Types to plot

• The Data Types and the Plotting

• Introduction to Statistics

• Analyzing Non-statistical and Statistical Analysis

• Major Categories of Statistics

• Statistics Analysis Aspects

• Sample and population

• Statistical Analysis Process

• Data Distribution

• Dispersion

• Histogram

• Testing

• Data Correlation, Inferential, and Correlation

• Anaconda

• Installing Anaconda Python Distribution

• Data Types and Python

• Fundamental Operators and Functionalities

• Numpy

• Making and printing a Ndarray

• Class and Attributes of Ndarray

• Basic Operation

• Activity-Slice It

• Views and Copy

• Mathematical Numpy Functions

• Analyzing the London Olympics Dataset

• Introduction to SciPy

• SciPy Sub Package - Integration and Optimization

• SciPy subpackage

• Calculating Eigenvalues and Eigenvector

• Identifying the SciPy subpackage

• Solving Linear Algebra problems using SciPy

• Conducting CDF and PDF with Scipy

• Introduction to Pandas

• Understanding DataFrame

• View and select data

• The missing values

• Data Operations

• Support for File Read and Write Support for File Read and Write

• Pandas SQLOperation

• Analyzing NewYork city fire department dataset

• The Introduction to Machine Learning Approach

• What is the Process?

• Supervised Learning Model Themes

• Supervised Learning Models for Linear Regression

• Supervised Learning Models for Logistic Regression

• Introduction to Models of Unsupervised Learning

• Pipeline

• Modell Persistence, Evaluation, and Assessment

• A model that can identify the signs of diabetes

• Introduction to NLP

• Applications of NLP

• NLP Libraries-Scikit

• Extraction Aspects

• Scikit learn-Model-Training and Grid-Search

• Sentiment Analysis with the help of NLP

• An Introduction to Data Visualization

• Line Properties

• (x,y) Subplots and Plots

• Different types of plots

• The creation of a pair plot using the seaborn library

• Internet Scraping, Parsing, and Web Scraping

• Understanding and searching the Tree

• The options for navigation

• How to navigate a Tree

• Modifying the Tree

• Printing and Parsing the Document

• Web Scraping of any website

• Understanding the reasons Big Data Solutions are Provided for Python.

• Components of Hadoop The core

• Python integration with HDFS making use of Hadoop Streaming

• Python integration with Spark by using PySpark

• Utilizing PySpark to determine Word Count

...and much more!

Course Content:

• Give an explanation of Data Science in detail
• Explain Data Analytics in detail
• Know what is Statistical Analysis and Business
• Know what Python Environment Setup and Essentials
• Define Mathematical Computing with Python
• Define Scientific Computing with Python
• Working in Data Manipulation with Pandas
• Learn about Machine Learning with Scikit-Learn
• Learn about the operation of Natural Language Processing with Scikit Learn
• Perform Data Visualization using Python with the help of Matplotlib
• Use Web Scraping using BeautifulSoup
• Know the Python integration with Hadoop MapReduce and Spark.

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