With the recent developments in Artificial Intelligence field, exponential growth has been seen in the job market for Artificial Intelligence and data Scientist related requirements. Even there are claims from some of the prominent job survey reports and leading Data science training institutes that there will be more than 11.5 million jobs that will be created in next few years for this stream.
Hence, looking at the trend more and more students, researchers, job seekers, existing employees (who want to equip new skills) are inclined to undergo trainings associated with Artificial Intelligence and Data Science.
Being one of the trend followers, I have invested lot of time looking for skill requirements, finding available courses and searching websites in order to achieve my goal. So, I thought of sharing these hoping that it may be of some help for aspiring Data Scientists.
Note: I have listed courses which seems useful to me and hence I highly recommend that blog readers personally check the Ratings and Review sections for the listed courses (especially the paid ones) before making any purchases.
Now coming back to our key agenda item, first step to learn any new trait or course is to understand the skills that are required to become an AI engineer or a Data Scientist.
Listed below are the key skills requirement and the available paid and free courses:
1. Statistics: Considering that AI/ML model relies on the algorithms which helps the machines/programs to identify the pattern in the massive datasets and based on the analysis it provides most potential outcomes for the input scenario.
Though you are not supposed to be a full-time mathematician or researcher to become AI/ML or data science expert, but knowledge of statistics definitely gives an edge. Some of the important topics from statistics will be Probability distribution, Variance, Standard deviation and Coefficient, Confidence intervals, mean, p-value, hypothesis testing etc.
Online Courses:
- Cognitive Class course (Free): https://cognitiveclass.ai/courses/statistics-101
- Khan Academy (Free): https://www.khanacademy.org/math/statistics-probability
- EDX (most of them Free to view but for certificate needs payment): https://www.edx.org/learn/statistics
2. Programming Skills: This is one of the essential components when you are pursuing the data scientist role or want to build a AI/ML models. Personally, I am more inclined towards Python and R due to ease of learning and these languages have vast ML libraries and packages respectively which can be directly used during programming.
- Anaconda Software: This is open source software distribution for Python and R programming languages. To create models you need a software where you can run your programming languages. I find Anaconda a lot more useful to run Python and R codes. It gives you flexibility to create your code on jupyter notebook and can run your scikit-learn, TensorFlow, and Theano models here. Its available for both windows and Mac. Link: https://www.anaconda.com/distribution/
Python Courses:
- Kaggle (Free): https://www.kaggle.com/learn/python
- Cognitive Class (Free): https://cognitiveclass.ai/courses/python-for-data-science
- Udemy (Paid): https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/
- Udemy (Paid): Machine Learning A-Z™: Hands-On Python & R In Data Science Course
R Courses:
- Cognitive Class (Free) : https://cognitiveclass.ai/courses/r-101
- Udemy (Paid): Machine Learning A-Z™: Hands-On Python & R In Data Science Course
3. Machine learning Algorithms: Machine learning algorithms are used to imitate the human learning process by creating a mathematical/statistical model using programming languages. These algorithms feed on training datasets in order to predict outcome on test datasets. Some of the most used algorithms are Linear and Logistic regression, Decision tree, K-Nearest Neighbour (KNN) clustering etc.
Online courses:
- Machine learning crash course from Google (Free): https://developers.google.com/machine-learning/crash-course
- Coursera (Mostly enrolment is free but certificate requires fees payment): Machine Learning course by Andrew Ng
- Kaggle (Free): https://www.kaggle.com/learn/intro-to-machine-learning
- Kaggle (Free): https://www.kaggle.com/learn/intermediate-machine-learning
- Kaggle (Free): https://www.kaggle.com/learn/machine-learning-explainability
- Cognitive Class (Free): https://cognitiveclass.ai/courses/machine-learning-with-python
- Cognitive class (Free): https://cognitiveclass.ai/courses/machine-learning-r
- Cognitive class (Free): https://cognitiveclass.ai/courses/machine-learning-dimensionality-reduction
- Udemy (Paid): Machine Learning A-Z™: Hands-On Python & R In Data Science
- Udemy (Paid): https://www.udemy.com/course/the-data-science-course-complete-data-science-bootcamp/
- Udacity (Introduction to Artificial Intelligence): https://www.udacity.com/course/intro-to-artificial-intelligence–cs271
4. Data wrangling: Building AI and Machine learning needs data and lot of it. Luckily with the availability of internet and online sharing, we have pool of massive inventory of data available. The only challenge here is that this data is in raw format and need to be cleaned and organized as per our requirement. Hence, data wrangling comes into picture.
5. Data visualization: Data visualization is the most important part of the Machine learning process. It helps us to gain insights on the feed data and help us to identify a pattern in the data set. There are various tools available for data visualization like Tableau, Python libraries Matplotlib or geoplot or ggplot, etc.
Course:
- Kaggle (Free): https://www.kaggle.com/learn/data-visualization
- Cognitive Class (Free): https://cognitiveclass.ai/courses/data-visualization-with-python
6. Practice: This is the most important step and you need to invest hours to train yourself to achieve your goal which can be to gat a new job in Data science or Artificial Intelligence OR learn a new course OR upskill yourself with emerging technologies.
I found “Kaggle: Your home for data science “ website very interesting. It gives you opportunities to learn by its free courses, discuss your queries- using its blogs and apply your knowledge by enrolling in the competitions.
Kaggle host competitions on its website where you can enrol yourself and create codes on the site itself using jupyter notebook extension. It’s a good way to learn and apply your knowledge to solve real world problems.
Link: https://www.kaggle.com/
Further Reading:
Tech Snippets 