Go for Data Science career. It has multiple opportunities

By ridhigrg |Email | Aug 1, 2019 | 1713 Views

Applied Data Science with Python Specialization
Gain new insights into your data. Learn to apply data science methods and techniques, and acquire analytical skills.
About this Specialization
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistically, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.

Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.

WHAT YOU WILL LEARN
  • Analyze the connectivity of a social network
  • Conduct an inferential statistical analysis
  • Discern whether a data visualization is good or bad
  • Enhance a data analysis with applied machine learning

Data Science Math Skills
About this Course
While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course, "Mastering Data Analysis in Excel," which is part of the Excel to MySQL Data Science Specialization.  Learners who master Data Science Math Skills will be fully prepared for success with the more advanced math concepts introduced in "Mastering Data Analysis in Excel."

Data science courses contain math-no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. 

Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.

Executive Data Science Specialization
Be The Leader Your Data Team Needs. Learn to lead a data science team that generates first-rate analyses in four courses.
About this Specialization
Assemble the right team, ask the right questions, and avoid the mistakes that derail data science projects.
In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You'll get a crash course in data science so that you'll be conversant in the field and understand your role as a leader. You'll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You'll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you'll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.

WHAT YOU WILL LEARN
  • Become conversant in the field and understand your role as a leader.
  • Recruit, assemble, evaluate, and develop a team with complementary skill sets and roles.
  • Navigate the structure of the data science pipeline by understanding the goals of each stage and keeping your team on target throughout.
  • Overcome the common challenges that frequently derail data science projects.

Statistics and Data Science
What you will learn
  • Master the foundations of data science, statistics, and machine learning
  • Analyze big data and make data-driven predictions through probabilistic modeling and statistical inference; identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making
  • Develop and build machine learning algorithms to extract meaningful information from seemingly unstructured data; learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks
  • Finishing this MicroMasters program will prepare you for job titles such as Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer

Learn data science by doing data science
What you will learn
  • How to load and clean real-world data
  • How to make reliable statistical inferences from noisy data
  • How to use machine learning to learn models for data
  • How to visualize complex data
  • How to use Apache Spark to analyze data that does not fit within the memory of a single computer

Source: HOB