Python Data Analysis Visualization Bootcamp
What you’ll learn
Perform Data Analytics seamlessly and smartly
Requirements
Experience in using a Computer – Windows/Linux/Mac
Basic Mathematical Knowledge
Description
Welcome to Data Analysis Analytics Bootcamp content powered by TakenMind. Are you interested to learn how zetabytes of data are processed by top tech companies to analyse data inorder to boost their business growth? Well, for a beginner you are at the right place and this is the most probably the right time for you to learn this. The average data scientist today earns $123,000 a year, according to Indeed research. But the operating term here is “today,” since data science has paid increasing dividends since it really burst into business consciousness in recent years.This course has its base on financial Analysis and the following concepts are covered:Python FundamentalsPandas for Efficient Data AnalysisNumPy for High Speed Numerical ProcessingMatplotlib for Data VisualizationPandas for Data Manipulation and AnalysisSeaborn Data VisualizationWorked-up examples.Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!You will learn how to:Import data setsClean and prepare data for analysisManipulate pandas DataFrameSummarize dataBuild machine learning models using scikit-learnBuild data pipelinesData Analysis with Python is delivered through lecture, hands-on labs, and assignments. It includes following parts:Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimentional arrays, and SciPy libraries to work with a various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.
Overview
Section 1: Setup and Jupyter Environment (Python 3)
Lecture 1 Introduction to the Study Kit
Lecture 2 #1 Downloading Setup and Installation
Lecture 3 #2 Installing Work Environment – Jupyter Notebook
Lecture 4 #3 Exploring Jupyter Notebook functionalities
Lecture 5 #4 Python Package Index – Using Command line interface and Jupyter Notebook
Section 2: Data Manipulation with Numpy (Python 3)
Lecture 6 #1 Getting Started – Numpy Arrays (Numerical Python)
Lecture 7 #2 Scalar Operations on Numpy Arrays
Lecture 8 #3 Array Indexes – Part 1
Lecture 9 #4 Array Indexes in Multi-Dimensional Numpy Arrays
Lecture 10 #5 – Premium Array Operations
Lecture 11 #6 Saving And Loading Arrays To External Memory
Lecture 12 #7 Statistical Processing And Sketching Graphs
Lecture 13 #8 Conditional Clauses And Boolean Operation
Section 3: Data Manipulation with Pandas (Python 3)
Lecture 14 #1 Getting Started with Series
Lecture 15 #2 Introduction to DataFrames in Pandas
Lecture 16 #3 Learning to access elements with indexes
Lecture 17 #4 – Re-indexing in pandas Series and Dataframes
Lecture 18 #5 – Dropping values from Series and DataFrames
Lecture 19 #6 – Handling Null or NAN values in pandas
Lecture 20 #7 Selecting and Modifying entries in Pandas
Lecture 21 #8 Coordinate and Regulate data in Series and Dataframes
Lecture 22 #9 – Ranking and Sorting in Series
Lecture 23 #10 Statistical Data Analysis and Graphs in Pandas
Section 4: Starting with File Operations (Python 3)
Lecture 24 #1 File Operations – Dataframes And Csv
Lecture 25 #2 Import Data From Excel File
Section 5: Data Analysis and Methodologies – Learn to perform Operations on datasets (Py 3)
Lecture 26 #1 Pandas – Merging along columns in DataFrames
Lecture 27 #2 Concatenation of Arrays, Series and Dataframes
Lecture 28 #3 Combining values of a DataFrame or Series
Lecture 29 #4 Reshaping Datasets – Series and Dataframe
Lecture 30 #5 Pivot Tables
Lecture 31 #6 Duplicates Analysis in dataset
Lecture 32 #7 Mapping in DataFrame
Lecture 33 #8 Replace values in Series
Lecture 34 #9 Renaming Indexes in DataFrame
Lecture 35 #10 Observation, Filtering and Basic Analysis
Section 6: Data Visualization
Lecture 36 Data Visualization and Introduction to Seaborn Visualization Library
Lecture 37 Histogram Visualization in seaborn
Lecture 38 Seaborn Kernel Density Estimation (KDE) Plot on Univariates
Lecture 39 Seaborn KDE Plot for multivariates
Lecture 40 Plotting multiple charts with seaborn
Lecture 41 Box Plot Visualization
Lecture 42 Regression Plots with seaborn
Lecture 43 Violin plot Visualization
Lecture 44 Heat Maps Visualization
Lecture 45 Cluster Map Visualization
Beginner Python Data Analyst should take up this course.,Intermediate Python Data Analyst should take up this course.
Course Information:
Udemy | English | 8h 6m | 4.32 GB
Created by: Siranjeevi – Python Data Analysis and Visualization
You Can See More Courses in the Business >> Greetings from CourseDown.com