Beginners Guide to Learn Data Science From Scratch and Master Numpy, Pandas, Matplotlib, Scikit-Learn and many more…
The advancement of technology has brought about an explosion in data collection and usage. Many industries rely on data science to develop more innovative and advanced products. In the last decade, the volume and variety of available data have increased dramatically, necessitating the development of new skills and the creation of entirely new occupations.
What you’ll learn
- Use Python for Data Science and Machine Learning.
- Learn to use NumPy for Numerical Data.
- Learn to use Pandas for Data Analysis.
- Learn to use Matplotlib for Python Plotting.
- Use Plotly for interactive dynamic visualizations.
- Use SciKit-Learn for Machine Learning Tasks.
Course Content
- Introduction to Data Science –> 4 lectures • 25min.
- Python Tools for Data Science –> 3 lectures • 20min.
- Operators in Python Programming –> 4 lectures • 20min.
- Strings in Python Programming –> 2 lectures • 14min.
- Decision Making Statements in Python –> 4 lectures • 19min.
- Loops and Functions in Python –> 3 lectures • 22min.
- List, Tuple, Set and Dictionary in Python –> 7 lectures • 47min.
- Sampling Data in Data Science –> 3 lectures • 18min.
- Data to CSV and TXT Formats –> 2 lectures • 20min.
- Data Preparation in Data Science –> 2 lectures • 20min.
Requirements
The advancement of technology has brought about an explosion in data collection and usage. Many industries rely on data science to develop more innovative and advanced products. In the last decade, the volume and variety of available data have increased dramatically, necessitating the development of new skills and the creation of entirely new occupations.
I am guessing you saw the hike too, and want in on the juicy tech space. You are in for a big treat. But this introduction will not be an introduction if we don’t know what we are dealing with. Allow me to introduce Data Science.
Data Science is a combo of several fields in IT where we use algorithms and scientific processes to extract facts from data and use them to create insights.
Data science entails using various techniques to draw conclusions from accumulated data. A data scientist’s job is to take an intricate business issue, distill the relevant information into data, and apply that data to the problem. You may wonder what this means for you personally and where to begin.
All that’s required is a head for ideas and a solid grasp of the ins and outs of a particular industry, both of which you undoubtedly possess. In data science, fraud, particularly online fraud, is a hot topic. Data scientists employ their expertise in this area by developing algorithms to monitor and prevent fraudulent activity. This data science beginner course will provide an excellent place to begin.
This comprehensive guide will teach you everything you need to know to get started in data science, from the various job opportunities available to data scientists to the practical applications of data science. You should begin this data science tutorial by reading up on the job description for a data scientist.
Many businesses and individuals are shifting their attention to big data and AI. It’s shocking to think that over 2.5 exabytes of data are produced and extracted by individuals and institutions daily. Since then, there has been a meteoric rise in the quantity of data. Most businesses have shifted to rely heavily on data to make decisions. As a result, some companies have established dedicated data-analysis divisions.
Statisticians conduct quantitative historical data analyses, which is still insufficient because the analysis’s findings would be limited to the present. Analysis was previously performed manually, but this task has been automated mainly with the advent of robust computing processes, cloud technology, and analytical tools. They started working on data analysis models.
Before delving into the many facets of data science, let’s grasp what it actually is. Data science, in its simplest definition, is the application of mathematics and statistics to large datasets to draw meaningful conclusions about patterns and relationships within the data. Using your programming, business, and analytical skills, you can manage and process the data set. You have to admit, this sounds challenging. Most people lack the knowledge and understanding necessary to work effectively with data science and improve their skills in this area.