Data Science is the art of uncovering the insights and trends that are hiding behind data. And with these insights, you can make strategic choices for a company or organization. The very exercise of going through analyzing data, trying to get some answers from it, is data science.
Data science is relevant today because we have tons of data available. We used to worry about lack of data, now we have a data deluge. In the past, we didn’t have algorithms, now we have algorithms. In the past, we couldn’t store large amounts of data, now, for a fraction of the cost, we can have gazillions of data sets for very low costs, so the tools to work with data, the very availability of data and the ability to store and analyze data, it’s all cheap, it’s all available. There’s never been a better time to be a data scientist. Data science is in a trend.
Machine Learning is likely the connection between data science and artificial intelligence since machine learning is the process of learning from data over time. However, it’s not the only thing connecting those two together. But, machine learning is the branch of AI that works best with data science
PREREQUISITES NEEDED TO LEARN DATA SCIENCE:
following guidelines stated by one of our alumni for the data science:
- Learn basic mathematics and statistics required for data science
- Develop a basic understanding of machine learning algorithms and solving real-time problems
- Learn SQL language and it’s various methods
- find Skills required to land your first data science internship/job
- Learn python/R language
One should know the importance of probability, linear algebra, numerical methods, statistics in real time problem solutions. Join various online python courses running over the internet free of cost which would help us to build understanding in data science and AI.
After perquisites and getting a stable grip over python/R language, one should start with Machine Learning. Enrol in various courses related to Machine Learning and Big data offered through MOOC platforms like courser, EdX, Udemy and Udacity. Learn about the big data and HADOOP. Following are the basic algorithms should be thoroughly studied:
- basic Machine Learning Algorithms.
- Linear Regression
- Logistic RegressioN
- Decision Trees
- KNN (K- Nearest Neighbours)
- Naïve Bayes
- Dimensionality Reduction
- Advanced algorithms(Deep Learning)
- Random Forests
- Dimensionality Reduction Techniques