10 Must have skills to become a Data Scientist.

• Read original article here

When you start writing a sentence you must be clear about the grammar concepts to build your sentences in the similar way statisticsis an essential concept for you to understand to build your high-quality models. The main advantage of statistics is that the information is presented in a organized way which will help us a lot.

Here are some basic concepts in statistics for becoming a Data Scientist!

Most machine learning models are built with several unknown variables. A knowledge of Calculusis significant for building a machine learning model.

Here are some basic concepts in calculus for becoming a Data Scientist!

It is necessary to understand Linear Algebra to step up into machine learning. With Linear Algebra you will be able to develop a better intuition for machine learning algorithms. Learning Linear Algebra would help you to choose the necessary parameters and develop a better model.

Here are some basic concepts in Linear Algebra for becoming a Data Scientist!

Programming gives us a way to communicate with our machines. So, you would have a question in yourself. Do you need to become the best in programming?. The answer is no.

Firstly you should choose a programming language. Python or R are the popular programming languages learnt by data scientists, each language has its own set of pros and cons. Python is a general purpose programming language, having multiple libraries with rapid prototyping makes it useful for data scientists. Ris basically a statistical analysis and visualization language.

Usually everyone start with Python as their primary language, because Python is found to be a easier language to perform machine learning tasks.

Data Manipulation which is also known as Data Wrangling is the skill where you clean your data and transform it into a format that can be useful for analyzing it. Data Manipulation takes a lot of time but it will really help you in taking better data driven decisions. Data manipulation is done on areas such as missing data, outlier treatments, correcting data types, scaling, transformation.

Data Analysis, it is the step where you understand a lot about your data. Data Analysis can be done in excel, SQL,usingPandaslibrary in Python.

Data Visualization is considered as the fun part in machine learning. To start with data visualization one must be familiar with histograms, bar plots, pie charts and move to advanced plots like waterfalls charts etc. These plots will be very useful during exploratory data analysis.

Data visualization is where you can relate your bi-variate and multi-variate variables with colors. Data Visualization can be done inTableau, Matplotlib etc.

For every data scientist in this world, Machine Learning is the core skill to have. For example, you want to predict the number of customers you will have in the next month by looking at the past month’s data, you will need to use machine learning algorithms.

You can start learning Machine Learning with simple algorithms like linear and logistic regressions and climb up to other models likeRandom forests, Gradient boosting, etc. It is really easy to remember the line of code for your machine learning algorithm which hardly takes only 3–4 lines of code but the most important thing is to know how they work.

According to the latest reports we are generating 2.5 Quintillions per day! This is due to the raise of internet, social media networks around us. This data is really very high in volume, veracity,andvelocity which forms the 3V’s in Big Data.

Every organization have been overwhelmed with such a large amount of data, to tackle this problem the organizations are rapidly adopting Big Data Technology so that the data can be stored properly and efficiently and used when needed.

Hadoop, Spark, Apache Storm,andHive are some of the Tools you must master.

Communication Skill is a soft skill. Communication Skill here refers to the skill with which you communicate with your fellow mates with data. Effective communication is necessary for quite a few reasons.

You can improve your communication skills by

The art of Story Telling is a very critical skill for every data scientist. Stories are truer than the truth. Every data scientists should also be a story teller because it brings in simplicity. Story telling makes our data interesting also stories provoke thought and bring out some useful insights on our data. This also helps in understanding the logic behind every data and analysis.

With time data analytics are growing bigger and better. It is expanding the number of people generating insights, increasing the need for more data storytellers in the future. Therefore, data scientist should not only stick to numbers and their analytical skills rather they should train themselves on to become a good story teller with the use of their data.