coursera specializations Courses

Coursera Data Science Specialization Courses

Starting is the biggest step to achieve any dream.  It’s 100% sure for the people who would like to step into the data science space.

If you were the data science beginner, who would like to learn the data science technologies to taste the real joy of working on data science projects. Then the very first question comes into your mind as a data science beginner is Where to Start.

The question where to start having tons of acceptable answers but the key is selecting the right ones. If you are a reading guy like me, you can choose the popular data science blogs or books for the perfect start.

The another way to the perfect start is taking MOOC courses from the sites like Coursera, Edx, Udacity, Udemy. Which were offering hundreds of courses on data science technologies. These courses are ranging from beginners to advanced level.

In this article, I am going to present you the popular data science specialization available in Coursera. I took few of them already. If you would like to see the courses I completed, please have a look at my LinkedIn Profile.

All these specialization courses were free to learn. If you would like to showcase your skills inthe form of certificates, you need to pay few $

Before we begin Let me explain why these course specialization so popular.

Why Coursera Specialization:

The ideal way to learn any new technology is to get the basics in the first phase. In the next phase use the basics to understand the advanced technologies or the new insights in these technologies. In the last stage, use all the knowledge you gained effectively to solve real world challenges.

The Coursera Data Science Specialization courses are also designed same like the above phases. The introductory courses teach the fundamental topics. Next, the advanced topics and finally the specialization ends with a challenging project that you need to complete.

To give you quick nap about what I am going to explain in this post is the below specialization and the order of courses in those specializations.

• Big Data specialization
  • Introduction to Big Data
  • Hadoop Platform and Application Framework
  • Big Data Integration and Processing
  • Machine Learning With Big Data
  • Graph Analytics for Big Data
• Machine Learning Specialization
  • Machine Learning Foundations: A Case Study Approach
  • Machine Learning: Regression
  • Machine Learning: Classification
  • Machine Learning: Clustering & Retrieval
• Data Science Specialization
  • Data Manipulation at Scale: Systems and Algorithms
  • Practical Predictive Analytics: Models and Methods
  • Communicating Data Science Results
• Data Analytics Specialization
  • Data Management and Visualization
  • Data Analysis Tools
  • Regression Modeling in Practice
  • Machine Learning for Data Analysis

Let’s check out in detail about all these specializations.

Big Data Specialization

Big Data Specialization

In this Big data Specialization, you will develop a robust set of skills that will allow you to process, analyze, and extract meaningful information from large amounts of complex data.

You will install and configure the below

• Hadoop with MapReduce,
• Spark,
• Pig and Hive,

You will learn the above open source tools basic usage and learn the graph analytics to model problems and perform scalable analytical tasks.

At the end of the specialization, you need to complete a capstone project with the knowledge you gained.

Now let’s walk through each course in this specialization.

COURSE 1:  Introduction to Big Data

Introduction to Big data is the first course in this big data specialization. In this course, you are going to learn the wide range of big data applications. The way big data tools are going to apply for the wide range big data related problems.

By the end of this course, you are going to set up the tools on your local system and will play with simple examples.

COURSE 2:  Hadoop Platform and Application Framework

In the second course of Big data Specialization, you are going to learn how to configure the Apache Hadoop Full stack and related tools. Following that you are going to write your first MapReduce program.

You will learn the basic concepts in distributed and parallel processing in Hadoop application framework. Will determine the significance of using the Hadoop framework in the parallel process the task and using the proper MapReduce programs.

Course Link:  Hadoop Platform and Application Framework

COURSE 3: Introduction to Big Data Analytics

Course Link: Introduction to Big Data Analytics

COURSE 4: Machine Learning With Big Data

In the Machine Learning with Big Data course, your will learn how to use the different open source tools to perform the parallel and distributed techniques. Following, you will learn how to apply machine learning algorithms on top these.

You will learn how to train, evaluate and validate the machine learning models build on using the big data tool. Will get the basics of Spark and Knime.

Course Link:  Machine Learning With Big Data

COURSE 5:  Introduction to Graph Analytics

In the last course of Big data Specialization, you will learn how to perform the graph analytics using the big data tools. This course will give you the overview of graph analytics. Followed by, you will learn how to store, retrieve and analyze graph structure data.

By the end of this course, you will be able to model different of graph model and performing graph analytics in a scalable manner.

Course Link:  Introduction to Graph Analytics

Machine Learning Specialization

In the machine learning specialization, you will be learning the machine learning algorithms like regression techniques, classification methods, and clustering techniques. In each course, you were going to learn these particular algorithms from basics to advance, and you will be going to implement these algorithm to solve the real world problems in small scale.

COURSE 1: Machine Learning Foundations: A Case Study Approach

This first course introduces the application of machine learning algorithms. This course is more like motivation to learning the machine learning techniques.

You will learn to dig deeper to understand the core insights in the data. As a practice, you will be going to predict the house precise based on house-level features. You will analyze the sentiment from user reviews.

COURSE 2: Regression

In this the regression course you will get the chance to learn in details about the regression techniques. You will learn the concepts of bias and variance. Estimate the model parameters using the optimization algorithms.

By the end of this course, you will train your own regression model to predict the house prices using the home related features like square footage, the number of bedrooms and the number of bathroom kind of features.

COURSE 3: Classification

In the classification course, you will learn about real world classification applications. Will learn the fundamental difference between the binary and multinomial classification techniques. You will be introduced to the concepts like logistic regression, support vector machine algorithms.

You will create a classification model to predict the sentiment of the review like the positive or negative sentiment. In Python, you will be going to implement this classification model.

 COURSE 4: Clustering & Retrieval

In the last course of machine learning specialization, you are going to learn the unsupervised classification algorithms like clustering. You will learn about the K-nearest neighbor algorithms.

In the process, you will learn how to use the k-nearest neighbors for both classification and regression algorithms.

You will learn how to implement the clustering algorithm to cluster the similar kind of documents using the different similarity measures.

Data Science at Scale Specialization

Data Science Specialization

Data Analysis and Interpretation Specialization

Data Analysis Specilization

In the data analytics specialization, you are going to learn the basic data science tools and techniques. These including data visualization, regression techniques, and machine learning concepts.