Data Analytics RealWorld Projects in Python
What you’ll learn
Get a job as a data Analyst on an average $156,000 after showcase these Projects on your Resume
By the end of this course you will understand the inner workings of the data analytics pipeline -joining,manipulating,filtering, extracting data ,Analysing Data
Learn how to work with various data within python, including: Excel Data,Geographical data,Text Data and Time Series Data Data
Be able to create in depth analyses with Pie charts, Bubble charts, Wordcloud and even geographical maps.
you will expertise in Pandas ,Seaborn, Matplotlib ,Plotly ,Folium, Geopy, Wordcloud and many other..
Solve any problem in your business, job or in real-time with powerful data analysis libraries
You will need to install Anaconda. We will show you how to do it in one of the first lectures of the course
This is the first course that gives hands-on Data Analysis Projects using Python..Student Testimonials:Excellent Course for Data Analytics Real World Projects.. specially in Python. Recommended Course for those who want to start or transform their career in Data Analytics- Alokkumar Mahatonice course, easy to understand lectures. Shan Singh was quick to respond to my questions too. i feel more confident compiling my portfolio now – Burutolu QI love how in 6 projects I went to 0 to 100% of knowledge of how to use some libraries and how to interpretate the results ! Also all my questions were answered ! Amazin course 110/100 – María Fernanda Villegas CascoUThe course was really helpful in learning data cleaning, data manipulation and data visualization. Newer library of plotly was something very new to me. while practizing with the dataset I got to know the pros of using plotly (simple, clear and interactive plots). Overall the coverage of the course with dataset from various industries and their problem set was very comprehensive and I think would match the industry scenario well. Also, I learned a whole new plethora of methods that are being called on class objects which I wasn’t familiar with before taking this course.Thanks for your support in Q&A as well.- Abhishek GajbhiyeCan you start right now?A frequently asked question of Python Beginners is: “Do I need to become an expert in Python coding before I can start working on Data Analytics Projects ? “The clear answer is: “No!You just require some Python Basics like data types, simple operations/operators, lists and numpy arrays that you can learn from my Free Python course ‘Basics Of Python’As a Summary, if you primarily want to use Python for Data Science/Data Analysis or as a replacement for Excel, then this course is a perfect match!Why should you take this Course?It explains Projects on real Data and real-world Data Science/Data Analytics Problems. No toy data! This is the simplest & best way to become a Data Analyst/Data ScientistIt shows and explains the full real-world Data. Starting with importing messy data, cleaning data, merging and concatenating data, grouping and aggregating data, Exploratory Data Analysis through to preparing and processing data for Statistics, Data Analytics , Machine Learning and Data Presentation.Professionals who are exposed to data but can’t yet leverage its powerProduct managers who want to make data-driven decisionsIt gives you plenty of opportunities to practice and code on your own. Learning by doing.In real-world Data Analytics projects, coding and the business side of things are equally important. This is probably the only course that teaches both: in-depth Python Coding and Big-Picture Thinking like How you can come up with a conclusion using various Data VisualisationGuaranteed Satisfaction: Otherwise, get your money back with 30-Days-Money-Back-Guarantee.
Section 1: Welcome to this Course !!
Lecture 1 Introduction & Course Benefits
Lecture 2 Utilize QnA of the course ( Golden Oppurtunity ) !
Lecture 3 How to follow this course-Must Watch
Lecture 4 Pre-requisites (Anaconda Python & Jupyter install & Set-up)
Lecture 5 Quick Summary of Jupyter Notebook
Section 2: Project 1->> Text Data Analysis ( Youtube Case-study )
Lecture 6 Overview of Problem Statement
Lecture 7 Download Dataset & Notebook
Lecture 8 Performing Sentiment Analysis
Lecture 9 Wordcloud representation of Sentiments
Lecture 10 Perform Emoji’s Analysis
Lecture 11 How to collect Entire data of Youtube..
Lecture 12 Analysing the most liked category !
Lecture 13 Lets Analyse whether audience is engaged or not !
Lecture 14 Analyzing trending videos !
Lecture 15 Does Punctuations have an impact on views, likes, dislikes ?
Section 3: Project 2->> Time Series Project ( Stock Market case-study )
Lecture 16 Overview of Problem Statement
Lecture 17 Download Dataset & Notebook
Lecture 18 Analyzing Closing Price of Stocks & Volume Trading
Lecture 19 Analyzing Daily returns
Lecture 20 Performing Multi-Variate Analysis
Lecture 21 Value at risk Analysis
Section 4: Project 3->> Geospatial Analysis Project ( Zomato Case-Study )
Lecture 22 Overview of Problem Statement
Lecture 23 Download Dataset & Notebook
Lecture 24 Data Pre-processing For Analysis
Lecture 25 In-depth analyis of Restaurant
Lecture 26 Analysing Most famous Restaurant
Lecture 27 Analysing Price of Restaurant
Lecture 28 Analysing insights from Restaurant Pattern
Lecture 29 Perform Spatial Analysis
Lecture 30 Perform Spatial Analysis Part2
Lecture 31 Analyzing Most Popular cuisines
Section 5: Project 4->> Sales Data Analysis ( E-commerce Case-study )
Lecture 32 Overview of Problem Statement
Lecture 33 Download Dataset & Notebook
Lecture 34 Preparing Data for Analysis
Lecture 35 Analyzing Monthly sales
Lecture 36 Analyzing Maximum Order & hour analysis
Lecture 37 Analysing Most sold Products
Section 6: Project 5->> IPL Data Analysis (Sports Case-study )
Lecture 38 Overview of Problem Statement
Lecture 39 Download Dataset & Notebook
Lecture 40 In-depth analyis of DA Warner (Batsman) performance
Lecture 41 Perform Analysis on Batsman-performance
Lecture 42 Perform Batsman-Comparison
Lecture 43 Perform Basic Analysis on IPL
Lecture 44 Perform Analysis across Seasons of IPL
Lecture 45 Comparitive Analysis of teams
Section 7: Bonus Lecture
Lecture 46 Bonus section
Everyone who want to step into Data Science/Data Analytics.,Anyone interested about the rapidly expanding world of data Analytics/Data Science,Everyone who want to switch Data Projects from Excel to Python (e.g. in Research/Science),Data Scientists/Data Analyst who want to improve their Data Handling/Manipulation/Analysis skills.,Data analysts and business analysts,Excel users looking to learn a more powerful software for data analysis
Udemy | English | 7h 6m | 3.39 GB
Created by: Shan Singh