May 23rd, 2020 | Updated on June 28th, 2022
Data analysis can be divided into four broad categories: diagnostic, descriptive, predictive, and prescriptive.
Why is data analysis so important? Because, an enormous amount of data gets generated, and business enterprises want to extract meaningful insights from these data.
As a data scientist, you are expected to examine data and perform mainly these four tasks:
- Gather hidden insights
- Improve business requirements
- Perform market analysis
- Generate reports.
Data analytics helps business enterprises make more-informed business decisions.
An organization acquires data through many different means. And it is the job of a data scientist or data analysis to extract meaningful information from this and provide insights.
Never has been data analysis more important than now as every organization is a data-driven organization.
Organizations are not satisfied with the data that flow to them. They want to gather more data related to their business interests and markets.
Organizations hire data experts who can store and categorize data and then derive patterns, connections that may be helpful for the organizations.
A good data analytics course should help you acquire skills in algorithms, statistics, visualization, and machine learning.
Here are the top data analytics courses which you can choose to start your career as a data scientist by studying data mining, big data applications, and data product development.
Data Analytics Courses
This course will show you how Statistical inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we’ll show you how to do this using R.
You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast.
What You’ll Learn
- The concepts necessary to define estimates and margins of errors of populations, parameters, estimates and standard errors in order to make predictions about data
- How to use models to aggregate data from different sources
- The very basics of Bayesian statistics and predictive modeling
This course prepares you to understand business analytics and become leaders in these areas in business organizations.
What You’ll Learn
After taking this course, students should be able to:
- approach business problems data-analytically. Students should be able to think carefully and systematically about whether and how data and business analytics can improve business performance.
- develop business analytics ideas, analyze data using business analytics software, and generate business insights.
This course will expose you to the data analytics practices executed in the business world. We will explore such key areas as the analytical process, how data is created, stored, accessed, and how the organization works with data and creates the environment in which analytics can flourish.
What You’ll Learn
This course also provides a basis for going deeper into advanced investigative and computational methods, which you have an opportunity to explore in future courses of the Data Analytics for Business specialization.
Skills you will gain: Data Model, Data Quality, Data Analysis, SQL
If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling and its connection to principal component analysis.
What You’ll Learn
- Mathematical Distance
- Dimension Reduction
- Singular Value Decomposition and Principal Component Analysis
- Multiple Dimensional Scaling Plots
- Factor Analysis
- Dealing with Batch Effects
Data Analysis with Pandas and Python offers 19+ hours of in-depth video tutorials on the most powerful data analysis toolkit available today. Lessons include: installing, sorting; filtering, grouping, aggregating, de-duplicating,
Pivoting, munging, deleting, merging, visualizing.
Data Analysis with Pandas and Python introduces you to the popular Pandas library built on top of the Python programming language.
What You’ll Learn
- Perform a multitude of data operations in Python’s popular “pandas” library including grouping, pivoting, joining and more!
- Learn hundreds of methods and attributes across numerous pandas objects
- Possess a strong understanding of manipulating 1D, 2D, and 3D data sets
- Resolve common issues in broken or incomplete data sets
In this course, part of the Big Data MicroMasters program, you will develop your knowledge of big data analytics and enhance your programming and mathematical skills. You will learn to use essential analytic tools such as Apache Spark and R.
Topics covered in this course include:
- cloud-based big data analysis;
- predictive analytics, including probabilistic and statistical models;
- application of large-scale data analysis;
- analysis of problem space and data needs.
What You’ll Learn
- How to develop algorithms for the statistical analysis of big data;
- Knowledge of big data applications;
- How to use fundamental principles used in predictive analytics;
- Evaluate and apply appropriate principles, techniques and theories to large-scale data science problems.
This popular training course—dramatically expanded and enhanced for 2018—teaches analysts and non-analysts alike the basics of data analytics and reporting. Robin Hunt defines what data analytics is and what data analysts do.
She then shows how to identify your data set—including the data you don’t have—and interpret and summarize data. She also shows how to perform specialized tasks such as creating workflow diagrams, cleaning data, and joining data sets for reporting.
What You Will Learn
, such as verifying data and conducting effective meetings, and common mistakes to avoid. Then learn techniques for repurposing, charting, and pivoting data. Plus, get helpful productivity-enhancing shortcuts and troubleshooting tips for the most popular data analytics program, Microsoft Excel.
The Certification of Professional Achievement in Data Sciences prepares students to expand their career prospects or change career paths by developing foundational data science skills.
This program is jointly offered in collaboration with the Graduate School of Arts & Science’s Department of Statistics, and The Fu Foundation School of Engineering & Applied Science’s Department of Computer Science and Department of Industrial Engineering & Operations Research.
Methods For Organizing Data
- Fundamentals of probability theory and statistical inference used in data science;
- Machine learning, with an emphasis on data science.
- Data visualization, the layered grammar of graphics, perception of discrete and continuous variables
Through expert instruction via boot camps, courses and hands-on labs, we prepare organizations and professionals to earn certifications. We offer certification training in AWS, Cisco, CompTIA, Google Cloud, ITIL®, Microsoft, Red Hat, VMware, and more. We also cover topics like cloud computing, cybersecurity, project management and more.
- What you will learn
- Agile and Scrum
- Business Analysis
- Cloud Computing
- Enterprise Architecture
- IT Service Management
- Networking and Wireless
- Project Management
To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series.
This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.
Unlike the rest of our Professional Certificate Program in Data Science, in this course, you will receive much less guidance from the instructors.
When you complete the project you will have a data product to show off to potential employers or educational programs, a strong indicator of your expertise in the field of data science.
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications.
It enables computational systems to adaptively improve their performance with experience accumulated from the observed data.
ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech.
This course balances theory and practice and covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion:
- What is learning?
- Can a machine learn?
- How to do it?
- How to do it well?
- Take-home lessons