Humans are the only social animals who always try to continuously progress and march ahead in life. There has been no stopping ever since the evolution of humans on this planet. Learning and Innovation have been key to the development of the human race over centuries. Last couple of decades the development has been full of IT & automation focussed. As the challenges arise so is the availability of solutions. The situation has come a level where we have humanoids to support us.
The most promising and evolving field is of Artificial Intelligence and Machine Learning. Data Analytics and visualization is also playing a crucial role in assisting and predicting major changes in weather conditions.
Python and R are the two most popular tools for Data Science. SQL is very useful for any data processing or data engineering work. GIT is useful for version controlling of codes.
This great document covers key concepts of all these tools in one handy available format. It covers Python, R, SQL, GIT etc. Please go through it and I’m sure you will find it useful.
4 best websites for learning Data Science:
1. CDAC ACTS : https://www.cdac.in/index.aspx?id=DBDA&courseid=83, www.iacsd.com
2. Coursera: bit.ly/coursera1234
3. DataCamp: bit.ly/datacamp123
4. Udemy: bit.ly/3qoXiWw
5. Edureka: https://lnkd.in/gAeJSR3
As a part of the training and learning community, we have multiple offers which would assist the aspirants to fulfill their dreams of a career in data science and analytics.
The options are listed below
- Self Study
- Learn from YouTube
- Learn from friend / mentor
- Learn from a finishing school ( www.iacsd.com )
- Learn as a part of the formal course being offered at IIT / NIT /IIIT etc.
- Subscribe to self learning tutorials
Multiple ways do exist to update, upskill, upgrade and learn. The most important part is the career options that are available post acquiring the required skills. To list a few career lines, I am sharing the Job roles available for the same
- Data Analyst
- Data Visualiser
- Cloud Computing/AWS
- Data Engineering
- BI/ DW
- E-commerce Analytics
- Data Science trainer
- ML/Data Scientist
- Tableau Expert
- R Programmer
- Scala/Spark Developer
- Business Analyst
The skills required to be considered for the above job roles can be explained below
Tableau / Power BI are one of the best tools for Data Visualisation along with Excel.
Python and R are two most popular tools for Data Science. SQL is very useful for any data processing or data engineering work. GIT is useful for version controlling of codes. Deep Learning is an exciting area of Data Science where a lot of innovation is happening. Neural Networks and AI along will NLP are some of the most exciting new technologies creating great value across domains.
One should note that Mathematics and Statistics are the foundations of Data Science and Machine Learning. One should learn key Statistics concepts. Many people directly jump into coding and models without understanding the underlying Mathematics and Statistics. That is not the correct approach. Understanding Mathematics and Statistics well is essential to become a good Data Scientist.
A good approach would be to learn the hypothesis testing, factor analysis, probability etc.
10 Commandments of Data Science ( Courtesy : https://www.linkedin.com/posts/activity-6814889468877770752-JDy2)
1. Focus mainly on solving the problem and not on tools, technologies, and models.
2. Data will never be clean or easily available. Data gathering and cleaning will take 80% of your time and effort.
3. Don’t underestimate the power of Excel and SQL – they are still two of the most useful tools for data analysis.
4. Simple models such as Linear or Logistic Regression will be good enough for many problems. You don’t need neural networks to solve every problem.
5. Textbook solutions may not work for most of the practical problems. You will need to try new approaches and innovate as required.
6. Nobody can remember everything. On the job, you can always use Google, Stack Overflow etc.
7. Learn Data Visualisation and develop the ability to explain your key insights in simple terms – such skills will be very useful with non-technical and business stakeholders.
8. Learn PowerPoint and storytelling – people may not appreciate your great work if you can’t convince them with your story.
9. Data Science is evolving rapidly. Please learn continuously, else you may become obsolete soon.
10. Focus mainly on solving the problem and not on tools, technologies, and models.