Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from both structured and unstructured data. It’s related to big data and data mining. Data Science involves unifying statistics, data analysis, machine learning and their related methods to understand and analyze actual phenomena with data. Data Scientists are highly educated. They earn a Bachelor’s degree. Most Data Scientists also have a Master’s degree or a PHD. These degrees will basically give you the skills that you need to process and analyze big data.
Data Scientists need to learn technical skills like Python, Hadoop platform, SQL, Machine Learning, Artificial Intelligence, Data Visualisation, etc.
It is a high level programming language that is based on interpreter. Python has a vast array of libraries that makes it a preferred language to learn for data science. It is a very easy language to learn. Python has scientific libraries like Pandas, Numpy, Matplotlib, SciPy, scikit-learn, etc. One should know about these scientific libraries to solve complex Python problems.
As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system or you need to send data to different servers. In such a case, Hadoop will help you. Hadoop is also used for data exploration, data filtration. Data sampling and summarization.
NoSQL and Hadoop are largely used in Data Science but still it is expected from Data Scientist to know SQL language so that he can write queries to fetch data from database. SQL is Structured Query language that is used to retrieve or modify data in the database. It is very important for Data Scientist to know how to retrieve data from Database.
It is a big data computation framework that is much like Hadoop but it is faster than Hadoop. Hadoop reads and writes to disk, which makes it slower whereas Spark caches its computations in memory. Using Apache Spark, data scientists can run their complicated algorithms faster.
R language is used especially for statistically oriented tasks. Statisticians learn this language for statistical analysis. One can learn and use R language for data analytics and statistics but there is a drawback also of this language. R is not a general purpose programming language. So, it is not used for tasks other than statistical programming.
Machine Learning & AI:
You need to learn machine learning techniques like supervised machine learning, decision tees, logistic regression, etc. These techniques will help you to solve different data science problems that are based on predictions of major organizational outcomes. Many Data Scientists are not master in Machine Learning. So, leaning Machine Learning will give you an advantage over others.
As a Data Scientist, you must be able to visualize data with the help of data visualization tools like ggplot, d3.js, Matplottlib, Tableau. Etc. These data visualization tools will help you to convert complex data into simpler form.
So, the data scientist should be highly educated and he should possess above technical skills to sell himself better in the market. These technical skills will help you to analyze and process data faster.