Python Data Analysis Visualization Bootcamp

Financial Data Analysis and Visualization with Python: The HighQuality Study kit
Python Data Analysis Visualization Bootcamp
File Size :
4.32 GB
Total length :
8h 6m



Siranjeevi - Python Data Analysis and Visualization


Last update

Last updated 12/2021



Python Data Analysis Visualization Bootcamp

What you’ll learn

Perform Data Analytics seamlessly and smartly

Python Data Analysis Visualization Bootcamp


Experience in using a Computer – Windows/Linux/Mac
Basic Mathematical Knowledge


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.


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

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