Top Salary backup can be some Data Science Courses

By ridhigrg |Email | Jun 25, 2019 | 2121 Views

Introduction to Data Science in Python
About this Course
This course will introduce the learner to the basics of the Python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating CSV files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as group by, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. 

This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

WHAT YOU WILL LEARN
  • Describe common Python functionality and features used for data science
  • Explain distributions, sampling, and t-tests
  • Query DataFrame structures for cleaning and processing
  • Understand techniques such as lambdas and manipulating CSV files

Data Science Specialization
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you'll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.

WHAT YOU WILL LEARN
  • Use R to clean, analyze, and visualize data.
  • Navigate the entire data science pipeline from data acquisition to publication.
  • Use GitHub to manage data science projects.
  • Perform regression analysis, least squares, and inference using regression models.

Open Source tools for Data Science
About this Course
What are some of the most popular data science tools, how do you use them, and what are their features? In this course, you'll learn about Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Experience. You will learn about what each tool is used for, what programming languages they can execute, their features and limitations. With the tools hosted in the cloud on Cognitive Class Labs, you will be able to test each tool and follow instructions to run simple code in Python, R or Scala. To end the course, you will create a final project with a Jupyter Notebook on IBM Data Science Experience and demonstrate your proficiency in preparing a notebook, writing Markdown, and sharing your work with your peers.

This course is part of multiple Specializations
A Specialization is a series of courses that help you master a skill. When you complete this course, your progress will count towards your learning in any of these Specializations:
Introduction to Data Science
IBM Data Science Professional Certificate

Databases and SQL for Data Science
About this Course
Much of the world's data resides in databases. SQL (or Structured Query Language) is a powerful language which is used for communicating with and extracting data from databases. Working knowledge of databases and SQL is a must if you want to become a data scientist.

The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment.  

The emphasis in this course is on hands-on and practical learning. As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python.

No prior knowledge of databases, SQL, Python, or programming is required.

Anyone can audit this course at no charge. If you choose to take this course and earn the Coursera course certificate, you can also earn an IBM digital badge upon successful completion of the course.

This course is part of multiple Specializations
  • A Specialization is a series of courses that help you master a skill. When you complete this course, your progress will count towards your learning in any of these Specializations:
  • Introduction to Data Science
  • IBM Data Science Professional Certificate

WHAT YOU WILL LEARN
  • Create and access a database instance on the cloud
  • Write basic SQL statements: CREATE, DROP, SELECT, INSERT, UPDATE, DELETE
  • Filter, sort, group results, use built-in functions, access multiple tables
  • Access databases from Jupyter using Python and work with real-world datasets

About this Course
Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand.

This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem-solving is relevant and properly manipulated to address the question at hand.

Accordingly, in this course, you will learn:
  • The major steps involved in tackling a data science problem.
  • The major steps involved in practicing data science, from forming a concrete business or research problem to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
  • How data scientists think!

This course is part of multiple Specializations
  • A Specialization is a series of courses that help you master a skill. When you complete this course, your progress will count towards your learning in any of these Specializations:
  • Introduction to Data Science
  • IBM Data Science Professional Certificate

Source: HOB