Basic principles of data science and machine learning with:
Python
1. Introduction to Data Science and Data:
What is data science and data, the role of data scientist in today’s world, machine learning vs data science vs data analysis, the salary range for the data scientist and the future of data science?
What will you learn – As a future data scientist, you should have a thorough knowledge of all these basic concepts. And this section will give you an overview of all the buzzwords of data science and the data science project workflow with the roles of Data Scientist, Machine Learning Engineer and Data Analyst.
2. Probability and Statistics:
What is a probability, the importance of probability in data science, various probability distributions and statistical concepts such as mean, mode, median and standard deviation.
What are you going to learn – Probabilities and statistics help bring logic to a world filled with chance and uncertainty. This section will teach you the concepts and techniques needed to understand and explore data that can be used in various fields such as data science, engineering and finance.
You will learn not only how to solve difficult technical problems, but also how to apply these solutions in everyday life.
3. Python Programming:
What is programming, introduction to the jupyter notebook, collections, keywords and variables, control flow instructions and python functions.
What are you going to learn – Python is a very simple language to learn and is the best language for data science and machine learning because of extremely powerful libraries.
to learn python programming and libraries : pandas, numpy, matplotlib.
4. Data collection and cleaning:
Read data from local file, CSV and Excel file, read JSON file, read data from API, detect missing data, process missing data.
What are you going to learn The most important task of the Data scientist is to collect data from different sources and to clean and prepare this data for analysis. You will learn how to collect data from local files, CSV and Excel, JSON file and API. But when you collect it, it will definitely be in a messy format. You will also learn how to clean this data in a simple way to spend less time cleaning the data and more time exploring and modeling the data.
5. Machine Learning:
What is machine learning, supervised or non superior learning and all the common machine learning techniques or algorithms in Python.
What are you going to learn Machine learning is everywhere, all the tech giants like Google, Facebook and Amazon are using the 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 learn from it.
In this course, you will learn the complete timeline of the data science project step by step. And with a simple explanation language and the use of real examples for better explanations will help you understand important concepts in a simple and understandable way.
Take a look at the few features of our course.
Hand-picked curriculum, specially designed for all levels of learners.
Continuous evaluation through stimulating quizzes.
Regular updates of the program.
Different aspects of data science explored.
explanations included implementation.
Understand how to solve data science problems in real life.
Suggestions are always welcome 🙂.
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