How to become a Data Scientist ? Skills required ?

How to Become a Data Scientist:

Data science is arguably the hottest career of the 21st century. In today’s high-tech world, everyone has pressing questions that must be answered. From businesses to non-profit organizations to government institutions, there is a seemingly-infinite amount of information that can be sorted, interpreted, and applied for a wide range of purposes.

Finding the right answers, however, can be a serious challenge.
How can a business sort through purchasing data to create a marketing plan? How can government departments use patterns of behavior to create engaging community activities? How can a non-profit best use their available marketing budget to further enhance their potential operations?
It all comes down to data scientists.
Because there is simply too much information for the average person to process and use, data scientists are trained to gather, organize, and analyze data, helping people from every corner of industry and every segment of the population.
Data scientists come from a wide range of educational backgrounds, but the majority of them will have technical schooling of some kind. Data science degrees include a wide range of computer-related majors, but it could also include areas of math and statistics. Training in business or human behavior is also common, which bolsters more accurate conclusions in their work.
There is a nearly infinite amount of information, and there is a nearly infinite amount of uses for data scientists. If you are intrigued by this captivating work, then let’s take a closer look at the career as a whole. Explore what they do, who they serve, and what skills they need to get the job done.

Steps to Become a Data Scientist:

There are three general steps to becoming a data scientist:
1. Earn a bachelor's degree in IT, computer science, math, physics, or another related field;

2. Earn a master's degree in data or related field;

3. Gain experience in the field you intend to work in (ex: healthcare, physics, business).

Pros & Cons:

There are many benefits to becoming a data scientist, and it doesn’t all center around pay. The job is a unique yet challenging career that offers a wide variety of daily tasks, and this variety is often cited as one of the main benefits. As a data scientist, you may work for a wide variety of companies, coming up with solutions and information related to customer retainment, marketing, new products, or general business solutions. This means you get to engage in unique and interesting topics and subjects that give you a wide perspective on the economy and world at large.

Just like any career, there are some clear drawbacks. While the extreme variety of subjects gives you new challenges, it can also mean that you never get to fully dive into a specific topic. The technologies that you use will be constantly evolving, so you may find that the systems and software that you just mastered are suddenly obsolete. Before you know it, you need to learn a whole new system. This can also lead to lots of confusion, as determining which systems are the best for specific jobs is very tough.

Skills Required:

• At least one programming language – R / Python.

• Mathematics ( Statistics, Probability, Linear Algebra, Differential Calculas, Discrete Maths, Numerical Analysis ).

• Data Pre-Processing.

• Machine Learning Algorithms.

• Advanced Machine Learning ( NLP , Deep Learning ).

1. R / Python:

With programming language, you can manipulate the data and apply certain algorithms to come up with some meaningful insights. Python and R are one of the most widely used languages by Data Scientists. The primary reason is the number of packages available for Numeric and Scientific computing and Visualaization. With the help of packages like Numpy, Pandas, Matplotlib, Seaborn, Scikitlearn in Python and e1071, rpart etc. in R, it becomes really easy to apply Machine Learning Algorithms. (Python recommended).

2. Mathematics:

Mathematics is very important in the field of data science as concepts within mathematics aid in identifying patterns and assist in creating algorithms. The understanding of various notions of Statistics, Probability, Linear Algebra, Differential Calculas, Discrete Maths, Numerical Analysis. Theory are key for the implementation of such algorithms in data science. Notions include: Regression, Maximum Likelihood Estimation, the understanding of distributions (Binomial, Bernoulli, Gaussian (Normal)) and Bayes’ Theorem.

Machine Learning is a field that focuses on computers having the ability to learn/operate without being programmed to do so.

3. Data Pre-Processing:

After cleaning Data pre-processing step starts where Feature Selection, Feature engineering and Exploratory Data Analysis is done.

Feature Selection is the process where you automatically or manually select those features which contribute most to your prediction variable or output in which you are interested in. Having irrelevant features in your data can decrease the accuracy of the models and make your model learn based on irrelevant features.

Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature engineering is fundamental to the application of machine learning, and is both difficult and expensive.

EDA is one of the crucial step in data science that allows  us to achieve certain insights and statistical measure that is essential for the business continuity, stockholders and data scientists. It performs to define and refine our important features variable selection, that will be used in our model.

4. Machine Learning And Advanced Machine Learning (Deep Learning):

Machine Learning, as the name suggests, is the process of making machines intelligent, that have the power to think, analyze and make decisions. By building precise Machine Learning models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

You should have good hands-on knowledge of various Supervised and Unsupervised algorithms.

Deep Learning has taken traditional Machine Learning approaches to a next level. It is inspired by biological Neurons (Brain Cells). The idea here is to mimic the human brain. A large network of such Artificial Neurons is used, this is known as Deep Neural Networks. Nowadays, most of the organizations ask for knowledge of Deep Learning, so don’t miss this.

Python is the most preferred language by Machine Learning experts, and TensorFlow, is one of the most famous Python libraries for creating Deep Learning Models.

Recommended Blogs:

1. Machine Learning for beginners. ( Click )

2. ML | Linear Regression using Python ( Click )

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