Python for Data Analytics for Beginners by Doing Projects

Python for Data Analytics for Beginners by Doing Projects, available at $34.99, has an average rating of 3.45, with 53 lectures, based on 28 reviews, and has 139 subscribers.

You will learn about Learn the usage of Jupyter Notebook environment. Learn Python programming from 0 to intermediate. Deal with different data sources: json, CSV, API Use Numpy library to create and manipulate arrays. Use Scipy library to create and manipulate data Use the pandas module with Python to create and structure data. Visualize data using matplotlib in Python. Make informed prediction with popular machine learning models in SKlearn. Write simple calculator using Python Predict the best location to open an Mexican restaurant using yelp data set Job referral to worlds top IT companies. This course is ideal for individuals who are People who like to learn Python programming language. or People who like to learn data analytics skills using Python. or Undergraduate who like to find a data analytics related job. or Young professionals who like to change to a data analytics related job. or Managers who like to use data analytics to help business growth. or Whoever do not want to be eliminated by Googles AI, join us to learn data and machine learning now It is particularly useful for People who like to learn Python programming language. or People who like to learn data analytics skills using Python. or Undergraduate who like to find a data analytics related job. or Young professionals who like to change to a data analytics related job. or Managers who like to use data analytics to help business growth. or Whoever do not want to be eliminated by Googles AI, join us to learn data and machine learning now.

Enroll now: Python for Data Analytics for Beginners by Doing Projects

Summary

Title: Python for Data Analytics for Beginners by Doing Projects

Price: $34.99

Average Rating: 3.45

Number of Lectures: 53

Number of Published Lectures: 53

Number of Curriculum Items: 53

Number of Published Curriculum Objects: 53

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the usage of Jupyter Notebook environment.

Learn Python programming from 0 to intermediate.

Deal with different data sources: json, CSV, API

Use Numpy library to create and manipulate arrays.

Use Scipy library to create and manipulate data

Use the pandas module with Python to create and structure data.

Visualize data using matplotlib in Python.

Make informed prediction with popular machine learning models in SKlearn.

Write simple calculator using Python

Predict the best location to open an Mexican restaurant using yelp data set

Job referral to worlds top IT companies.

Who Should Attend

People who like to learn Python programming language.

People who like to learn data analytics skills using Python.

Undergraduate who like to find a data analytics related job.

Young professionals who like to change to a data analytics related job.

Managers who like to use data analytics to help business growth.

Whoever do not want to be eliminated by Googles AI, join us to learn data and machine learning now

Target Audiences

People who like to learn Python programming language.

People who like to learn data analytics skills using Python.

Undergraduate who like to find a data analytics related job.

Young professionals who like to change to a data analytics related job.

Managers who like to use data analytics to help business growth.

Whoever do not want to be eliminated by Googles AI, join us to learn data and machine learning now

You will use different tools for different projects in this course. You will experience the full workflow as a data analyst, including data selection, data manipulation, data visualization for report using real-word dataset.?After the course, you will have extensive experience on what are the common task and operations as an data analyst.

Anaconda is?needed before course start (Installation Manual and Video Tutorial are provided). Download related data set and documents before starting each topic.

Course Curriculum

Chapter 1: Environment set up for Mac and Windows

Lecture 1: Environment set up for Mac and Windows

Chapter 2: Python Programming 0 to Intermediate – Part I

Lecture 1: Introduction to Python Projects

Lecture 2: Introduction to Python Project 1

Lecture 3: Start Python Project 1 – Your Own Calculator

Chapter 3: Python Programming 0 to Intermediate – Part II

Lecture 1: print, variables, data types

Lecture 2: operators, control flow & function

Lecture 3: loops, scope, list, tuple, dictionary & set

Chapter 4: Python Intermediate

Lecture 1: file processing

Lecture 2: modules and import

Lecture 3: lambda function and usage

Lecture 4: map, reduce and filter

Lecture 5: use database in Python

Lecture 6: Exercise 1 – Flatten List [Answer]

Lecture 7: Exercise 2 – Summary Ranges [Answer]

Chapter 5: Numpy Basics

Lecture 1: numpy nasics

Lecture 2: introduction to pandas

Lecture 3: pandas dataframe

Lecture 4: Exercise 3 – build dictionary from DB [Answer]

Chapter 6: Data processing using pandas & visualization using matplotlib

Lecture 1: introduction to pandas

Lecture 2: pandas – series

Lecture 3: pandas – dataframe

Lecture 4: pandas – index

Lecture 5: pandas – load data & save data

Lecture 6: pandas – load json data and load json data from API

Lecture 7: pandas – load data from database

Lecture 8: pandas – indexing, dropping, loc, iloc, arithmetics

Lecture 9: pandas – functions, sorting&ranking, duplicated index

Lecture 10: pandas – statistics

Lecture 11: pandas – filling missing data

Lecture 12: pandas – data transformation

Lecture 13: pandas – binning, dummies, string manipulation

Chapter 7: Introduction to Scipy with data set

Lecture 1: Introduction to scipy

Lecture 2: scipy – linear algebra

Lecture 3: scipy – interpolation

Lecture 4: scipy – optimization

Lecture 5: scipy – statistic

Lecture 6: scipy – summary

Chapter 8: Sklearn with data set and hands on practices

Lecture 1: sklearn – introduction

Lecture 2: sklearn – premier to machine learning

Lecture 3: sklearn – machine learning problems

Lecture 4: sklearn – use case – digits recognition

Lecture 5: sklearn – estimator

Lecture 6: sklearn – metrics

Lecture 7: sklearn – hyper-parameter tuning

Lecture 8: sklearn – bias/variance trade off

Chapter 9: Integrated project using yelp dataset – optimal place for Mexican restaurant

Lecture 1: project – introduction

Lecture 2: project – load data into SQLite

Lecture 3: project – view business insights through data set

Lecture 4: project – load and combine data

Lecture 5: project – learning from other restaurants

Lecture 6: project – look into an example : Edinburgh

Lecture 7: project – make prediction

Lecture 8: project – process text content and make decision

Instructors

Xcourse Team
Pave the way to your future career!

Rating Distribution

1 stars: 3 votes

2 stars: 6 votes

3 stars: 3 votes

4 stars: 7 votes

5 stars: 9 votes

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