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Course overview
Have you ever wondered how companies like Google, Amazon, extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need? Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - its interesting work too!
What Is
Data Science? Who Is Data Scientist?
Data Science is all about mining hidden insights of data
pertaining to trends, behavior, interpretation, and inferences to enable
informed decisions to support the business. The professionals who perform these
activities are said to be a Data Scientist / Science professional. Data Science
is the most high-in-demand profession and as per Harvard and the most sort
after a profession in the world.
Why One
Should Take The Data Science Course?
Is Data
Science certification is worth pursuing as a career?
The answer is a big YES for myriad reasons. Digitization across
the domains is creating tons of data and the demand for the Data Science
professionals who can evaluate and extract meaningful insights is increasing
and creating millions of jobs in the space of Data Science. There is a huge
void between the demand and supply and thereby creating ample job opportunities
and salaries. Data Scientists are considered to be the highest in the job
market. Data Scientist career path is long-lasting and rewarding as the data
generation is increasing by leaps and bounds and the need for the Data Science
professionals will increase perpetually.
This course focuses on "how to build and understand",
not just "how to use". Anyone can learn to use an API in 15 minutes
after reading some documentation. It's not about "remembering facts",
it's about "seeing for yourself" via experimentation. It will teach
you how to visualize what's happening in the model internally. If you want more
than just a superficial look at machine learning models, this course is for
you.
Who this course is for:
Students and professionals who want to apply machine learning
techniques to their datasets
Students and professionals who want to apply machine learning
techniques to real-world problems
Anyone who wants to learn classic data science and machine
learning algorithms
Anyone looking for an introduction to artificial intelligence (AI)
Why wait?
Every day is a missed opportunity.
Click
“Enroll Now” and join others in our community to get a leg up in the industry,
and learn Data Scientist and Machine Learning.
What will i learn?
- Gain an in-depth understanding of data structure and data manipulation
- Understand the different components of the Hadoop ecosystem
- Learn to analyze data using Tableau and become proficient in building interactive dashboards
- Master the concepts recommendation engine, and time series modeling and gain practical mastery over principles, algorithms, and applications of Machine Learning
- Understand the pros and cons between classic machine learning methods and deep learning
- Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models
- Unfold the power of deep neural networks
- Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
Requirements
- No prior experience is needed (not even Math and Statistics). We start from the very basics.
- A computer (Linux/Windows/Mac) with internet connection.
- Two paths for those that know programming and those that don't.
- Passion for success
Curriculum for this course
88 Lessons
00:00:00 Hours
Data Science in Real Life
9 Lessons
00:00:00 Hours
-
Defining Data Science
.
-
What Does a Data Science Professional Do?
.
-
Data Science in Business
.
-
Use Cases for Data Science
.
-
Data Science People
.
-
Working with NumPy arrays
.
-
Web Scraping with BeautifulSoup
.
-
Adding calculations to your workbook
.
-
Apache Flume and HBase
.
Statistics Essentials
17 Lessons
00:00:00 Hours
-
Introduction
.
-
Sample or population data?
.
-
The fundamentals of descriptive statistics
.
-
Measures of central tendency, asymmetry, and variability
.
-
Practical example: descriptive statistics
.
-
Distributions
.
-
Estimators and estimates
.
-
Confidence intervals: advanced topics
.
-
Practical example: inferential statistics
.
-
Hypothesis testing: Introduction
.
-
Hypothesis testing: Let’s start testing!
.
-
Practical example: hypothesis testing
.
-
The fundamentals of regression analysis
.
-
Subtleties of regression analysis
.
-
Assumptions for linear regression analysis
.
-
Dealing with categorical data
.
-
Practical example: regression analysis
.
R Programming for Data Science
5 Lessons
00:00:00 Hours
-
R basics
.
-
Data structures in R
.
-
R Programming fundamentals
.
-
Working with Data in R
.
-
Stings and Dates in R
.
Data Science with R
10 Lessons
00:00:00 Hours
-
Introduction to Business Analytics
.
-
Introduction to R Programming
.
-
Data Structures
.
-
Data Visualization
.
-
Statistics for Data Science-I
.
-
Statistics for Data Science-II
.
-
Regression Analysis
.
-
Classification
.
-
Clustering
.
-
Association
.
Python for Data Science
4 Lessons
00:00:00 Hours
-
Python Basics
.
-
Python Data Structures
.
-
Python Programming Fundamentals
.
-
Working with Data in Python
.
Data Science with Python
11 Lessons
00:00:00 Hours
-
Data Science Overview
.
-
Data Analytics Overview
.
-
Statistical Analysis and Business Applications
.
-
Python Environment Setup and Essentials
.
-
Mathematical Computing with Python (NumPy)
.
-
Scientific computing with Python (Scipy)
.
-
Data Manipulation with Pandas
.
-
Machine Learning with Scikit–Learn
.
-
Natural Language Processing with Scikit Learn
.
-
Data Visualization in Python using matplotlib
.
-
Python integration with Hadoop MapReduce and Spark
.
Machine Learning
10 Lessons
00:00:00 Hours
-
Introduction to Artificial Intelligence and Machine Learning
.
-
Data Wrangling and Manipulation
.
-
Supervised Learning
.
-
Feature Engineering
.
-
Supervised Learning-Classification
.
-
Unsupervised learning
.
-
Time Series Modelling
.
-
Ensemble Learning
.
-
Recommender Systems
.
-
Text Mining
.
Tableau Desktop 10
7 Lessons
00:00:00 Hours
-
PGetting Started With Tableau
.
-
Working With Tableau
.
-
Deep diving with Data and Connections
.
-
Creating Charts
.
-
Mapping data in Tableau
.
-
Dashboards and Stories
.
-
Visualizations For An Audience
.
Big Data Hadoop and Spark Developer
15 Lessons
00:00:00 Hours
-
Introduction to Big Data and Hadoop Ecosystem
.
-
HDFS and Hadoop Architecture
.
-
MapReduce and Sqoop
.
-
Basics of Impala and Hive
.
-
Working with Hive and Impala
.
-
Type of Data Formats
.
-
Advanced HIVE concept and Data File Partitioning
.
-
Apache Pig
.
-
Basics of Apache Spark
.
-
RDDs in Spark
.
-
Implementation of Spark Applications
.
-
Spark Parallel Processing
.
-
Spark RDD Optimization Techniques
.
-
Spark Algorithm
.
-
Spark SQL
.
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About instructor
Student feedback
Reviews
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Nagesh SuryavanshiI have taken this whole data science course they have really helps to understand the basics and emphasize to go deeper.
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