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This article lists the best programs and courses in data science that you can take to improve your skills and get one of the best data scientist jobs in 2023. You should take one of these online courses and certifications for data scientists to get started on the right path to mastering data science.
Top Data Science Courses
In this section we will discuss some the popular courses for data science that are available on the internet.
A variety of factors/aspects were considered when producing the list of top data science courses for 2023, including −
Curriculum Covered − The list is compiled with the breadth of the syllabus in mind, as well as how effectively it has been tailored to fit varied levels of experience.
Course Features and Outcomes − We have also discussed the course outcomes and other aspects, such as Query resolve, hands-on projects, and so on, that will help students obtain marketable skills.
Course Length − We have calculated the length of each course.
Skills Required − We have addressed the required skills that applicants must have in order to participate in the course.
Course Fees − Each course is graded based on its features and prices to ensure that you get the most value for your money.
Mastering the A-Z of Data Science & Machine LearningCourse Highlights
Covers all areas of data science, beginning with the fundamentals of programming (binary, loops, number systems, etc.) and on through intermediate programming subjects (arrays, OOPs, sorting, recursion, etc.) and ML Engineering (NLP, Reinforcement Learning, TensorFlow, Keras, etc.).
Lifetime access.
30-Days Money Back Guarantee.
After completion certificate.
Course Duration: 94 hours.
Check the course details here
Mastering Python for Data Science & Data Analysis
Course Highlights
This course will enable you to build a Data Science foundation, whether you have basic Python skills or not. The code-along and well planned-out exercises will make you comfortable with the Python syntax right from the outset. At the end of this short course, you’ll be proficient in the fundamentals of Python programming for Data Science and Data Analysis.
In this truly step-by-step course, every new tutorial video is built on what you have already learned. The aim is to move you one extra step forward at a time, and then, you are assigned a small task that is solved right at the beginning of the next video. That is, you start by understanding the theoretical part of a new concept first. Then, you master this concept by implementing everything practically using Python.
Become a Python developer and Data Scientist by enrolling in this course. Even if you are a novice in Python and data science, you will find this illustrative course informative, practical, and helpful. And if you aren’t new to Python and data science, you’ll still find the hands-on projects in this course immensely helpful.
Course Duration: 14 hour
Check course details here.
R Programming for Data Science
Course Description
The course demonstrates the importance and advantages of R language as a start, then it presents topics on R data types, variable assignment, arithmetic operations, vectors, matrices, factors, data frames and lists. Besides, it includes topics on operators, conditionals, loops, functions, and packages. It also covers regular expressions, getting and cleaning data, plotting, and data manipulation using the dplyr package.
Lifetime access.
30-Days Money Back Guarantee.
After completion certificate.
Course Duration: 6 hours
Check the course details here.
In this course you will learn about −
Life Cycle of a Data Science Project.
Python libraries like Pandas and Numpy used extensively in Data Science.
Matplotlib and Seaborn for Data Visualization.
Data Preprocessing steps like Feature Encoding, Feature Scaling etc...
Machine Learning Fundamentals and different algorithms
Cloud Computing for Machine Learning
Deep Learning
5 projects like Diabetes Prediction, Stock Price Prediction etc...
Course Duration: 7 hours
Check the course details here.
Mastering Data Science with Pandas
Course Description
This Course of Pandas offers a complete view of this powerful tool for implementing data analysis, data cleaning, data transformation, different data formats, text manipulation, regular expressions, data I/O, data statistics, data visualization, time series and more.
This course is a practical course with many examples because the easiest way to learn is by practicing! then we'll integrate all the knowledge we have learned in a Capstone Project developing a preliminary analysis, cleaning, filtering, transforming, and visualizing data using the famous IMDB dataset.
Course Duration: 6 hours
Check the course details here.
Python and Analytics for Data Science.
This course is meant for beginners and intermediates who wants to expert on Python programming concepts and Data Science libraries for analysis, machine Learning models etc.
They can be students, professionals, Data Scientist, Business Analyst, Data Engineer, Machine Learning Engineer, Project Manager, Leads, business reports etc.
The course have been divided into 6 parts - Chapters, Quizzes, Classroom Hands-on Exercises, Homework Hands-on Exercises, Case Studies and Projects.
Practice and Hands-on concepts through Classroom, Homework Assignments, Case Studies and Projects
This Course is ideal for anyone who is starting their Data Science Journey and building ML models and Analytics in future.
This course covers all the important Python Fundamentals and Data Science Concepts requires to succeed in Academics and Corporate Industry.
Opportunity to Apply Data Science Concepts in 3 Real World Case Studies and 2 Real World Projects.
The 3 Case Studies are on Loan Risk Analysis, Churn Prediction and Customer Segmentation.
The 2 Projects are on Titanic Dataset and NYC Taxi Trip Duration.
Course Duration: 8.5 hours
Check the course details here.
Data Science-Fundamentals of Statistics
Course Description
Students will gain knowledge about the basics of statistics
They will have a clear understanding of different types of data with examples which is very important to understand data analysis
Students will be able to analyze, explain and interpret the data
They will understand the relationship and dependency by learning Pearson's correlation coefficient, scatter diagram, and linear regression analysis between the variables and will be able to know to make the prediction
Students will understand the different methods of data analysis such as a measure of central tendency (mean, median, mode), a measure of dispersion (variance, standard deviation, coefficient of variation), how to calculate quartiles, skewness, and box plot
They will have a clear understanding of the shape of data after learning skewness and box plot, which is an important part of data analysis
Students will have a basic understanding of probability and how to explain and understand Bayes theorem with the simplest example
Course Duration: 7 hours
Check the course details here.
Top Data Science ebooks
In this section we will discuss some the popular ebooks for data science that are available on the internet.
Beginners Course on Data Science
In this book, you'll find everything you need to know to get started with data science and become proficient with its methods and tools. Understanding data science and how it aids prediction is crucial in today's fast-paced world. The purpose of this book is to provide a high-level overview of data science and its methodology.Data Science has its origins in statistics. However, expertise in programming, business, and statistics is necessary for success in this arena. The best way to learn is to familiarize yourself with each subject at length.
Finding trends and insights within a dataset is an age-old art. The ancient Egyptians used census information to better levy taxes. Nile flood predictions were also made using data analysis. Finding a pattern or exciting nugget of information in a dataset requires looking back at the data that came before it. The company will be able to use this information to make better choices.The need for data scientists is no longer hidden; if you enjoy analyzing numerical information, this is your field. Data Science is a growing field, and if you decide to pursue an education in it, you should jump at the chance to work in it as soon as it presents itself.
Check the ebook here.
Building Data Science Solutions With Anaconda
In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works.
By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
Check the ebook here.
Practical Data Science With Python
The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.
As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.
By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.
Check the ebook here.
Cleaning Data for Effective Data Science
The book dives into the practical application of tools and techniques needed for data ingestion, anomaly detection, value imputation, and feature engineering. It also offers long-form exercises at the end of each chapter to practice the skills acquired.
You will begin by looking at data ingestion of data formats such as JSON, CSV, SQL RDBMSes, HDF5, NoSQL databases, files in image formats, and binary serialized data structures. Further, the book provides numerous example data sets and data files, which are available for download and independent exploration.
Moving on from formats, you will impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features that are necessary for successful data analysis and visualization goals.
By the end of this book, you will have acquired a firm understanding of the data cleaning process necessary to perform real-world data science and machine learning tasks.
Check the ebook here.
Essentials of Data Science And Analytics
This book combines the key concepts of data science and analytics to help you gain a practical understanding of these fields. The four different sections of the book are divided into chapters that explain the core of data science. Given the booming interest in data science, this book is timely and informative.
Check the ebook here.