The Complete Data Science Course 2020: Unsupervised Machine Learning

Become an expert in Data Science by deep diving into the nuances of data interpretation, inter working technologies like Machine Learning, and mastering powerful programming skills to take your career in Data Science to the next level

Advanced 5(1 Ratings) 1 Students enrolled
Created by kunal Narhare Last updated Wed, 27-May-2020 English
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

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
  • 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 basics
  • Data structures in R
  • R Programming fundamentals
  • Working with Data in R
  • Stings and Dates in R
  • 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 Basics
  • Python Data Structures
  • Python Programming Fundamentals
  • Working with Data in Python
  • 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
  • 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
  • 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
  • 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
  • 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
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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.

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About the instructor
  • 11 Reviews
  • 21 Students
  • 8 Courses
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Student feedback
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  • Sat, 30-May-2020
    Nagesh Suryavanshi
    I have taken this whole data science course they have really helps to understand the basics and emphasize to go deeper.
Rs24999 Rs40000
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  • 00:00:00 Hours On demand videos
  • 88 Lessons
  • Full lifetime access
  • Access on mobile and tv