Become a Confident Data Scientist and grab the highly paid jobs of 2021 and future!
Data roles are one of the highest-paid individuals in the tech industry. For the last four years, data Analysis has been featured as a top career by Glassdoor. What’s more, Employment of Data Analysis and statisticians is projected to grow 35 percent from 2019 to 2029, much faster than the average for all occupations.
- · Data Analysis helps professionals and businesses take data-driven intelligent decisions
- · Data Analysis helps in justifying the decisions taken (unlike trial-and-error shots).
- · Can be implemented across various industries and companies, without any procedural difference.
- · Data Analysis helps you understand the real time data, visualize from ppyplot, geoplot, the expected outcomes.
- · Helping organizations solve complex issues that have come up with the monumental amount of data that has been generated.
- · Reduce the chance for errors - with Statistics controlling the decisions, you have a better chance of making the right decision that would yield favourable results.
- · Not just this, Data Analysis helps you and your business plan for anomalies/unwanted circumstances beforehand.
- · Have a better relationship with the consumer. With data, you will be able to help them better with purchase history, purchase suggestions, etc.
We cover a wide variety of topics, including:
NumPy: Array Creation, Indexing Slicing, Broadcasting, Array Manipulation, Sorting, Searching, Swapping and Linear algebra
Pandas: Data Structures, Data frame, Panel, Descriptive Statistics, Reindexing, Window Functions, Sparse Data, Caveats & Gotchas
Matplotlib: Pyplot API, Simple Plot, PyLab module, Object-oriented Interface, Grids, Three-dimensional Plotting, Working with Images and Transforms
Visualization: Seaborn, Plotly, geoplots
Example Curriculum
- Introduction (7:36)
- Environment (9:27)
- Envirnoment Notes
- Ndarray Object (10:27)
- Ndarray Object Notes
- Data Types - Part 1 (9:11)
- Data Types - Part 2 (8:32)
- Data Types Notes
- Array Attributes (7:42)
- Array Atributes Notes
- Array Creation (11:18)
- Array Creation Notes
- Array Creation using Existing Data - Part 1 (7:32)
- Array creation using Existing Data - Part 2 (7:32)
- Array creation using Existing Data Notes
- Array creation using numerical value - Part 1 (8:36)
- Array creation using numerical value - Part 2 (6:57)
- Array Creation from numerical ranges Notes
- Array Indexing and Slicing - Part 1 (9:47)
- Array Indexing and Slicing - Part 2 (7:11)
- Advanced Indexing (11:04)
- Advanced Indexing Notes
- Indexing & Slicing Notes
- Broadcasting (10:19)
- Broadcasting Notes
- Broadcasting Iteration (7:14)
- Broadcasting Iteration Notes
- Array manipulation - Part 1 (7:47)
- Array manipulation - Part 2 (7:10)
- Array manipulation Notes
- Binary Operation (11:09)
- Binary Operation Notes
- String Operation (9:18)
- String Operation Notes
- Airthmatic and statistical Function (6:59)
- Arithmatic & Statistical Function Notes
- Sort, Searching and Counting Function - Part 1 (10:58)
- Sort, Searching and counting Function - Part 2 (6:36)
- Sort, Searching and Counting Notes
- Downloadable IPYNB File Notes
- Introduction (7:48)
- Environment Setup (7:15)
- Working with real data (6:21)
- Data Structure (7:19)
- Data Structure Notes
- Series (8:02)
- Data Frame (6:43)
- Working with Series and Data Frame with real dataset (8:23)
- Panel - Part 1 (7:00)
- Panel - Part 2 (7:00)
- Panel - Part 3 (6:32)
- Panel - Part 4 (6:32)
- Series Functionality (7:10)
- DataFrame Functionality (8:39)
- Example of Series and Data Frame(with Rael Dataset) Notes
- Basic Functionality(Series and Data Frame) Notes
- Statistics Methods (7:37)
- Statistics Method Notes
- Lambda Function (6:40)
- Reindexing (9:50)
- ReIndexing (1) Notes
- ReIndexing (2) Notes
- Iteration (6:36)
- Iterration method Notes
- Sorting (6:24)
- Sorting Notes
- MultiAxes Indexing - Part 1 (6:41)
- MultiAxes Indexing - Part 2 (6:40)
- Multiaxes Indexing Notes
- Windows Statistics Methods (9:37)
- Windows Statistics Method - Checkpoint Notes
- Missing Values (6:45)
- Timedelta (7:49)
- Timedelta Notes
- Introduction to Visualization (7:16)
- Plots - Part 1 (6:10)
- Plots - Part 2 (7:33)
- Plots - Part 3 (6:17)
- Plotting Notes
- Downloadable IPYNB File Notes
- Introduction (5:32)
- Enviroment Setup (7:49)
- Simple Plot - Part 1 (7:13)
- Simple Plot - Part 2 (9:48)
- Pylab - Part 1 (7:59)
- Pylab - Part 2 (7:31)
- Pylab Notes
- Pylab-checkpoint Notes
- Grid (8:36)
- Bar plot (7:05)
- Histogram (7:06)
- Pie chart (6:01)
- Scetter plot (8:31)
- Contour plot (7:15)
- Box Plot (7:22)
- Quiver Plot (7:07)
- Violinplot (6:59)
- plot(matplotlib -2D) Notes
- plot(matplotlib -2D)-checkpoint Notes
- 3D plot introduction (6:29)
- Lines and Scatter plot using 3D (7:10)
- Counter Plot(3D) (6:32)
- Wireframes and Surface plots (7:44)
- Surface traingulation(3D plot) (7:09)
- 3D plotting Notes
- 3D plotting-checkpoint Notes
- Downloadable IPYNB File Notes
- Introduction to Seaborn (7:08)
- Matplotlib vs Seaborn (7:04)
- Colorplot and Histogram Plot (7:01)
- Statistical Estimation (6:35)
- Seaborn (plots- grid) (7:32)
- Plotly Introduction (5:52)
- Plotly Example (6:46)
- Seaborn_Plotly_Geoplot Notes
- Seaborn_Plotly_Geoplot Checkpoint Notes
- Downloadable IPYNB File Notes
- Introduction (7:09)
- Types of data (6:24)
- Mean, Median and Mode (6:39)
- Statistics Example (6:45)
- Function (6:23)
- Skewness (7:12)
- Statistics Analysis (6:53)
- Types of Statistics Analysis (7:40)
- Statistics Notes
- Statistics Checkpoint Notes
- Linear Regression - Part 1 (6:50)
- Probability (7:29)
- Linear Regression - Part 2 (9:28)
- Bayer's Theorem (7:00)
- Adictive Theorem (6:51)
- Multiplicative Theorem (6:31)
- T Test (6:15)
- F Test (6:11)
- Z Test (4:46)
- Z Test Vs T Test (6:30)
- Linear Regression Notes
- Linear Regression Checkpoint Notes
- Downloadable IPYNB File Notes
Frequently Asked Questions
When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.