Data Science with Python Course
Learner rating  9 

 Course platform: Simplilearn
 Level: Advanced
 Full lifetime access
 Paid course
 Class length: Approx. 68 hrs.
About Data Science with Python Course
In this course on Data Science with Python, you will master the concepts of Python programming. Through this Python for Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing. After completion of this course, you will master the essential tools of Data Science with Python.
Moreover, at the end of the course, you will be subjected to a project where you can apply your knowledge thought in the class to solve realworld problems. Let’s begin with the introduction to data science and why to learn data science with python.
What is Data Science?
Data science provides meaningful information based on large amounts of complex data or big data. Data science combines different fields of work in statistics and computation to interpret data for decisionmaking purposes. A data scientist collects, analyzes, and interprets large volumes of data, in many cases, to improve a company’s operations. Data science professionals develop statistical models that analyze data and detect patterns, trends, and relationships in data sets.
About Python
Python is an objectoriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options.
Why learn Data Science with Python?
Python is one of the most popular languages in Data Science, which can be used to perform data analysis, data manipulation, and data visualization. Python offers access to a wide variety of data science libraries and it is the ideal language for implementing algorithms and the rapid development of applications
Course Content
 Course Overview
 Program Features
 Delivery Mode
 Prerequisites
 Target Audience
 Key Learning Outcomes
 Certification Details and Criteria
 Course Curriculum
 Course End Projects
 Customer Reviews
Syllabus
Lesson 01 Data Science Overview
 Introduction to Data Science
 Different Sectors Using Data Science
 Purpose and Components of Python
Lesson 02 – Data Analytics Overview
 Data Analytics Process
 Exploratory Data Analysis (EDA)
 EDAQuantitative Technique & EDA Graphical Technique
 Data Analytics Conclusion, Communication or Predictions
 Data Types for Plotting
Lesson 03 – Statistical Analysis and Business Applications
 Introduction to Statistics, Statistical, Nonstatistical, and Analysis Major Categories of Statistics
 Statistical Analysis Considerations and Population and Sample
 Statistical Analysis Process and Data Distribution
 Dispersion and Histogram Knowledge
 Check Testing Knowledge, Correlation and Inferential Statistics
Lesson 04 – Python Environment Setup and Essentials
 Anaconda
 Installation of Anaconda Python Distribution (contd.)
 Data Types with Python & Basic Operators and Functions
Lesson 05 – Mathematical Computing with Python (NumPy)
 Introduction to NumPy, ActivitySequence it Right, and Creating Printing an array
 Class and Attributes of nd array & Basic Operations
 Views Mathematical Functions of NumPy
Lesson 06 – Scientific computing with Python (SciPy)
 Introduction to SciPy
 SciPy Sub Package – Integration and Optimization
 Demo – Calculate Eigenvalues and Eigenvector with Demo & Assignment on Statistics, Weave, SciPy, and IO.
Lesson 07 – Data Manipulation with Pandas
 Introduction to Pandas
 Understanding the Data Frame
 View and Select Data Demo
 Missing Values, Data Operations Knowledge, Check File Read and Write Support
 Pandas SQL Operation Assignment and Demos
Lesson 08 – Machine Learning with Scikit Learn
 Understand data sets and extract its features
 Identifying problem type and learning model & Test and optimizing the model
 Supervised Learning Model Considerations with Demo & Assignment
Lesson 09 – Natural Language Processing with Scikit Learn
 NLP Overview
 NLP Applications Knowledge and Check NLP Libraries Scikit Extraction Considerations
Lesson 10 – Data Visualization in Python using matplotlib
 Introduction to Data Visualization
 Check Line Properties (x,y) Plot and Subplots Knowledge & Check Types of Plots
 Analyze the “auto mpg data” and draw a pair plot using ‘seaborn’ library for mpg, weight, and origin
Lesson 11 – Web Scraping with Beautiful Soup
 Web Scraping and Parsing
 Understanding and Searching the Tree Navigating options
 Demo & Assignments on Navigating a Tree Knowledge & Modifying the Tree Parsing
Lesson 12 – Python integration with Hadoop MapReduce and Spark
 Why Big Data Solutions are Provided for Python
 Hadoop Core Components and Python Integration with HDFS using Hadoop streaming
 Using Hadoop Streaming for Calculating Word Count & Python Integration with Spark using PySpark
What you will learn from Data Science with Python Course
 Data wrangling
 Exploration of data
 Data visualization
 Mathematical computing
 Web scraping
 Hypothesis building
 Python programming concepts
 NumPy and SciPy package
 ScikitLearn package for Natural Language Processing
Prerequisites
To best understand the data science with python course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science.
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