Fundamentals of Data Science & Machine Learning with Python, Learn Data Science, Probability & Statistics, Python, Data Gathering & Cleaning, Machine Learning & Data Visualization
Created by Aditya Khedkar
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If I will tell the scope and future of data science in the World is very high and Data Scientist is the most in-demand profession today, I am sure you won’t trust. What if a great leader says so. According to Tim Berners Lee, the inventor of the World Wide Web–
"Data is a Precious Thing and will Last Longer than the Systems themselves."
Also, Vinod Khosla, an American Billionaire Businessman and Co-founder of Sun Microsystems declared –
"In the next 10 years, Data Science and Software will do more for Medicines than all of the Biological Sciences together."
By the above two statements, it is clear that data proliferation will never end and because of that, the use of data related technologies like Data Science and Big Data is increasing day by day. Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!
Data science is a multidisciplinary field. It encompasses a wide range of topics. So Data scientist also needs to be multidisciplinary
Understanding of the data science field and the type of analysis carried out based on project objectives
Statistics and Probability
Applying advanced statistical techniques in Python
WHY SHOULD I ENROLL FOR THIS COURSE?
In this course, we will start right from the basics like what is Data Science? Most of the people are often confused when they are asked - “WHAT IS DATA SCIENCE?” The most common reply is - “UM, MACHINE LEARNING. ” Well, Machine Learning is a part of Data science domain but it doesn’t mean that machine learning is the synonym of data science.
Things you will learn
So in this course, you will learn all the basic concepts related to data science in a step by step approach.
1. Introduction to Data Science and Data
What is Data Science and Data, Role of Data Scientist in today's world, Machine learning vs Data Science vs Data Analysis, Salary range for Data scientist and Future of Data Science?
what will you learn - As a future data scientist you should have a profound knowledge of all these basic concepts. And this section will give you an overview of all the buzzwords of data science and the working flow of data science project with the roles of Data Scientist, Machine Learning engineer and Data analyst in them.
2. Probability and Statistics
What is a probability, the importance of probability in data science, various probability distributions and statistical concepts like mean, mode, median and standard deviation.
what will you learn - Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This section will give teach you the concepts and techniques needed to understand and explore data which can be used in various fields like data science, engineering, and finance. You will learn not only how to solve challenging technical problems, but also how you can apply those solutions in everyday life.
3. Python Programming
What is programming, Intro to jupyter notebook, Collections, keywords and variables, control flow statements and functions in python.
what will you learn - Python is a very simple language to learn and it is the best language for data science and machine learning because of extremely powerful libraries. You will learn the basics of python programming and various libraries like pandas, numpy, matplotlib.
4. Data Gathering and Cleaning
Reading Data from Local file, CSV and Excel file, Reading from JSON file, Reading Data from an API, Detecting Missing Data, Handling missing data.
what will you learn- The most important task of Data scientist is collecting data from different sources and clean and prepare that data for analysis. You will learn how to gather data from local files, CSV and Excel, JSON file and API. But when you collect any it will be definitely in a messy format. So you will also learn how to clean that data in a simple way so that you can spend less time on cleaning data and more time on exploring and modeling data.
5. Machine Learning
What is Machine Learning, Supervised vs Unsupersivesd learning and All common machine learning techniques or algorithms in Python.
what will you learn- Machine learning is everywhere, all the tech giants like Google, Facebook and Amazon use machine learning model to give users a personalized experience. In this section, you will learn different machine learning models from which you can train your data and find insights from it.
In this course, you will learn the entire data science project timeline in a step by step manner. And with a simple explanation language and use of real-life examples for better explanation purposes will help you to understand the important concepts in a simple and relatable manner.
Have a look at the few features of our course.
Handpicked curriculum, specially designed for all levels of learners.
Continuous assessment through challenging quizzes.
Get your questions answered within 48 hours.
A variety of resources such as useful links, books, PDFs are also provided.
Regular updates made to the curriculum.
Different aspects of Data Science explored.
Step by Step implementation with explanations included.
Understand how can you solve data science problems in real life.
Suggestions are always welcome :)
● WHO CAN ENROLL FOR THIS COURSE?
This course is not intended for a specific group of people. Anyone who wants to learn about Data Science can enroll for this course. If you already know a few concepts, you can always revisit the ideas and clear them. We have covered statistics along with technical topics. In short, this is a perfect course for you if you want to kick start your career in Data Science!