Welcome to Data Science for Beginners! In this course, you\\\’ll learn the basics of data science, from data visualization to data modelling to data wrangling. You\\\’ll start with the big picture of what data science is and why people go into it, and then move on to the specifics of data visualization, statistics, and data modelling. You won\\\’t just learn the tools and techniques of data science, you\\\’ll also learn how to work with data and turn it into actionable knowledge.
It’s no secret that data is a crucial component of today’s modern society. From understanding user behaviour on the web to uncovering molecular interactions in the body, data is behind many of today’s biggest discoveries and biggest businesses. Unfortunately, knowing how to find, clean, and use data isn’t enough to turn you into a data scientist. It takes years of study and practice to become an expert.
Data science is a field that focuses on the study and manipulation of data. Whether you’re a budding web developer who wants to build a better Google or a finance analyst who wants to build a better stock market, data science can help you solve a variety of problems. Data science is also a field that’s expected to grow at a staggering rate over the next decade, with a reported shortage of more than 400,000 workers in the United States alone. If you want to be a part of this exciting field, this course is for you.
What is Data Science?
Data Science is a field of study that involves applying diverse scientific methods, algorithms, and processes to extract insights from large amounts of data. It aids in the discovery of hidden patterns in raw data. The evolution of mathematical statistics, data analysis, and large data has given rise to the phrase Data Science.
Data Science is a multidisciplinary field that allows you to extract knowledge from both structured and unstructured data. You can use data science to turn a business problem into a research project and then back into a practical solution.
Let’s learn what each role entails in detail:
Role: A Data Scientist is a professional who manages enormous amounts of data to come up with compelling business visions by using various tools, techniques, methodologies, algorithms, etc.
Languages: R, SAS, Python, SQL, Hive, Matlab, Pig, Spark
Role: The role of a data engineer is of working with large amounts of data. He develops, constructs, tests, and maintains architectures like large scale processing systems and databases.
Languages: SQL, Hive, R, SAS, Matlab, Python, Java, Ruby, C + +, and Perl
Role: A data analyst is responsible for mining vast amounts of data. They will look for relationships, patterns, and trends in data. Later he or she will deliver compelling reporting and visualization for analyzing the data to make the most viable business decisions.
Languages: R, Python, HTML, JS, C, C+ + , SQL
Role: The statistician collects, analyses, and understands qualitative and quantitative data using statistical theories and methods.
Languages: SQL, R, Matlab, Tableau, Python, Perl, Spark, and Hive
Role: Data admin should ensure that the database is accessible to all relevant users. He also ensures that it is performing correctly and keeps it safe from hacking.
Languages: Ruby on Rails, SQL, Java, C#, and Python
Role: This professional needs to improve business processes. He/she is an intermediary between the business executive team and the IT department.
Languages: SQL, Tableau, Power BI and, Python