Data Science using Python Course Overview
Learn coding in Python for Data Science and Data Analytics with extensive hands on tutorials. The skills that you will acquire with this course are Statistical Analysis, Data Importing, and Visualization with different libraries. In this course you will move your way up from the basics, that is, ranging from touching all the aspects of integer, float, strings, logical functions, and other types along with learning loops which is for() and while() loops to understanding the complete syntax and applying them through our use cases and exercises for the exact purpose for practice in Jupyter Notebook. This course is designed in a completely different manner as compared to other courses on Data Science using Python. Here, every section is designed step by step. Every new section begins with the built-up on previous sections so as to maximize our practice hence climbing one step after the other. After every lecture, you will learn a constructive concept and it comes with a catch! You can apply the concepts that we have learned right away in our fascinating real-life examples, in addition to this you will be facing some valuable analytical challenges which you will learn to solve and gain practical experience, out of this some of them will be demonstrated in the lecture and the others will be homework exercise. The course is designed in such a way that you don’t need any programming skills and you will watch yourself climbing the way up and be successful in completing the course with the homework exercises and quizzes lined up especially for you.
What problems will Python with Data Science solve?
In case if you are seeking a stimulating new career that offers a wide variety of opportunities, the data science industry is the one for you. These days for the organizations to grow, they all rely on the insights that are derived from a large set of data, filter them according to their purpose that is for what premise they are actually deriving the insights so as to make informed evaluations, plan for the future aspects, and so on. The data scientists are the ones who import, process, filter, and organize the data with algorithms, some scientific methods, and statistics which may in addition to this involve some other techniques.
Basically, Python is the programming language used widely by the data scientists so as to confront daily challenges as it holds the capability to provide step by step output of the codes that you have worked on. It is among the chief data science tools used across businesses and industries.
Python excels in the domain of programming languages when it comes to scalability. It facilitates that as it provides the data scientist with flexibility and different ways to approach problems. Python community is one of the main reasons the language is getting up a notch as more users are volunteering in creating more and more data science libraries. This assists in the creation of modern tools and improved processing techniques that are widely utilized today and these are the reasons for the exact purpose for people preferring Python for data science. Python brings around many visualization options in which Matplotlib is the sole and solid foundation around other libraries. These visualization libraries and packages help in converting the data into as expressive / presentable form by creating charts and graphical plots.
In case, if you are looking out for a non – volatile industry that isn’t going anywhere momentarily then data science is an excellent choice.
Who should take this course?
-Programmers, Developers, Architects
-Analytics Manager leading the team of analysts
-Business Analysts
-You should take this course if you are worn out of other Python courses being too much complicated
-If you need to learn python in a completely hands-on manner
-If you think taking challenges excite you
Prerequisites
No prior knowledge or skills of coding are required; all you need is a passion for coding and working with data.
What will you learn by the end of the course?
-Learn programming in Python at a significant level.
-Download and analyze the data programmatically.
-Step by step visualization of data.
-How to work around with Jupyter Notebooks.
-Core principles of programming and analysis.
-Learn about the integer, float, strings, and other data types in Python.
-Create Variables.
-Working your way around with different libraries such as Matplotlib, NumPy, Pandas, Seaborn, and so on.
-How to create loops that are while() and for() along with conditional statements.
-How to install packages in Python.
