The rapid pace at which technology is advancing and progressing is not news to people living in this age. Artificial Intelligence and machine learning are dominantly running the world functions and will continue to do more so in the future.
Each new day brings advancements in the field of information technology that opens new doors and opportunities for individuals seeking to pursue this field as a career path. Data Science is another technology that is leading the fourth industrial revolution.
- What is Data Science?
- Best Data Science Courses
What is Data Science?
New data is being generated each day at an unthinkable speed and in huge amounts. This data is of great relevance to the businesses and data science is a major source of capitalizing on this available data. Since we live in a data-driven world, every decision made in our surroundings is a calculated move based on the accumulated data.
Data science is a multifaceted field of data processing that makes use of quantifiable systems and technical standards. The resulting outcome is invaluable information that can help in business decision-making, strategic planning, and other uses.
Best Data Science Courses
There are many skills required in order to become a skilled data scientist, which is a highly lucrative career option as it is currently one of the highest-paying jobs in the world. Since it is a continuously evolving field and one that is highly in demand, you need to constantly be ready to dive into new learning experiences that lead to continued growth and success.
We have put together a list of the best data science courses that we feel you need to look at, regardless of you being a beginner or an experienced professional. The only way to guarantee success in this field is through constant learning, and these courses may just be that extra something that your career needs at the moment.
This course gives a detailed in-depth information regarding Python, visualization, and statistical learning concepts that are required for all projects related to data science. The value provided by the course goes well with the price it comes at and is a good learning investment.
You will be required to work on assignments on Jupyter notebook workbooks for a solid grasp of the taught concepts. A follow-up solutions video is then posted by the instructor that explains each part of the assignment in detail to avoid any confusion.
The curriculum includes the following topics among others:
- Python Crash Course
- Python for Data Analysis – Numpy, Pandas
- Python for Data Visualization – Matplotlib, Seaborn, Plotly, Cufflinks, Geographic plotting
- Data Capstone Project
- Machine learning – Regression, KNN, Trees and Forests, SVM, K-Means, PCA
- Recommender Systems
- Natural Language Processing
- Big Data and Spark
- Neural Nets
- Deep Learning
2- Data Science Specialization- JHU @ Coursera
This course uses the R programming language and is a perfect blend of both theory and application which makes it a popular choice amongst interested individuals. It also has a complete section on statistics which is an integral component of data science but is usually skipped out in many courses.
If you have some understanding of algebra and programming experience, then you must take this course. Some of the topics included in the curriculum are:
- The Data Scientist’s Toolbox
- R Programming
- Getting and Cleaning Data
- Exploratory Data Analysis
- Reproducible Research
- Statistical Inference
- Regression Models
- Practical Machine Learning
- Developing Data Products
- Data Science Capstone
3- Introduction to Data Science- Metis
It is a six-week-long accredited course taught live by a data scientist employed at one of the top companies. Not only is this course accredited, but it also provides a certificate upon completion and continued educational units.
This in-depth course covers every topic that is included in the process of data science. The live class is conducted like a regular college class every week and additional support is provided to students who are struggling with some concept or are facing difficulties.
Some of the topics included in the curriculum are:
- Computer Science, Statistics, Linear Algebra Short Course
- Exploratory Data Analysis and Visualization
- Data Modelling: Supervised/Unsupervised Learning and Model Evaluation
- Data Modelling: Feature Selection, Engineering, and Data Pipelines
- Data Modelling: Advanced Supervised/Unsupervised Learning
- Data Modelling: Advanced Model Evaluation and Data Pipelines | Presentations
Prior knowledge of Python, algebra and basic statistics is a must for this course otherwise, you will surely feel lost.
4- Applied Data Science with Python Specialization-UMich @ Coursera
This specialization course is focused on the applied side of data science providing an insight into the data science Python libraries like matplotlib, pandas, NLTK, sci-kit-learn, and networkX with real data application opportunities.
Since this course does not offer stats as a component, it is best that you dust up on your statistical skills or take up a stats course on the side to have a better understanding of the taught concepts.
The courses offered are:
- Introduction to Data Science in Python
- Applied Plotting, Charting & Data Representation in Python
- Applied Machine Learning in Python
- Applied Text Mining in Python
- Applied Social Network Analysis in Python
5- Statistics and Data Science MicroMasters- MIT @ edX
Unlike many other courses being offered online and also on this list that does not offer statistical courses, this series is loaded with probability and statistics, dedicating more time towards statistical content.
Before taking up this series, you must be familiar with Python and both single and multivariate Calculus. Since it does not offer any courses regarding introduction to Python, it is recommended that you familiarize yourself with this programming language before enrolling in this series for a more enriched learning experience.
The series is available free of cost and also with a price including certification and graded material. There will be an exam at the end of the series to wrap up the whole learning journey comprising of several projects.
The courses included in the series are:
- Probability – The Science of Uncertainty and Data
- Data Analysis in Social Science—Assessing Your Knowledge
- Fundamentals of Statistics
- Machine Learning with Python: From Linear Models to Deep Learning
- Capstone Exam in Statistics and Data Science
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