In today’s era, there is plenty of data generating every day. The main aim of data science is to convert unstructured data into structured data and to highlight the main points.
It first understands the behaviour of the data and then uses it correctly for the improvement of business. It includes data analytics, data mining, machine learning, deep learning, Artificial Intelligence, and other associated subjects. For understanding it better, one should have hands on the knowledge in mathematics, computer skills, and most notably, statistics.
It is growing in demand among people that is increasing with each passing day and also growing in popularity and hence, becoming more competitive in the field of job.
Now we need to understand what is data analytics?
We define data analytics, firstly as, analyzing the available raw data by mechanical processes and algorithms and finally making it structural informative data. It also reveals the important information that is needed to be analysed and that is valuable for the business system which would otherwise have got lost in the mass of information.
Then we come across the term artificial intelligence.
By artificial intelligence we means, the machine stimulates the human brain and acts according to their actions along with it exhibits certain traits like learning and problem solving which results in rationalising and achieving a specific goal.
With this we come down to Machine Learning.
Now both machine learning and artificial intelligence are correlated as the machine automatically learn and improve from experience which results in development of computer programs which accesses data without any human help. So for this first observation of data is required such as examples, direct experience, or instruction, in order to make better decisions in the future.
categories of machine learning as follows:
⦁ supervised machine learning algorithms
⦁ unsupervised machine learning algorithms
⦁ semi-supervised machine learning algorithms
⦁ reinforcement machine learning algorithms
Finally we come down to statistics.
To understand the concept of Data Science, one has to have knowledge about statistics.Since, we have a lot of raw data which is needed to be filtered and give a structural shape to it so, it processes the raw data and helps the data scientists and analysts to bring out the meaningful information from the raw data by performing some mathematical computations.
Some related topics are as follows:
Then who is a DATA SCIENTIST?
To be a data scientist, all the above terms we used need to be joined together.
He should be capable to analyse, process and model data and extract the most relevant part of the data among the mass data.Also should be knowledgable in computer science, statistics, and mathematics.
They should have the ability to use industry knowledge, contextual understanding, skepticism of existing assumptions and to increase the chances of business.
A data scientist must have the skill of converting raw messy huge unstructured into a structured form by sources like smart devices, social media feeds.
Experience with Hadoop and Spark is always important.
Here is a list of skills you need to become an efficient data scientist:
1. Statistical knowledge
You should be well-equipped in distributions, statistical tests, and likelihood estimators to handle data-driven businesses.
2. Programming languages
You are expected to be familiar with programming languages, like Python, R, and SQL and it is the basic prerequisite to become a professional in this field.
3. Machine Learning
To manage huge amounts of data, data scientists must be powerful with Machine Learning techniques and methodologies.
4. Visualization and communication
These are the two most important skills where visualization eases the making of data-driven decisions and communication skills work on how to impress the audience. Tableau is a popular visualization tool, data scientists must be familiar with.
5. Data Wrangling
Sometimes working with data becomes problematic because of cluttered and imperfect arrangements. To a data scientist, it is highly important to know how to cope up with imperfections in data. A skilled professional has competent about how to manage imperfect data and how to organize them using the right techniques.
You have extra facilities when you are very strong in linear algebra and multivariable calculus.
7. Communication skills
To get preference in a company as a data scientist, one has to have hands-on data analytics, mathematics, statistics, machine learning, and also should have excellent communication skills to interact with clients.