How to Become a Data Scientist : Most demanding Job in Future with High Salary

Becoming a Data Scientist varies by industry, but there are some common skills and training that will give you the boost you need to start your career in Data Science. You want to become a data scientist and looking for High Salary? Then this article for you...

What is a Data Scientist?

Data Scientists are responsible for discovering information from massive amounts of structured and unstructured data to help define or meet specific business needs and goals. The job of the Data Scientist becomes increasingly important as companies increasingly use data analysis to guide their decisions and rely on automation and machine learning ( Machine Learning ) as essential components of their IT strategies.

The job of the Data Scientist

The main goal of the Data Scientist is to organize and analyze large amounts of data, often using software specifically designed for this task. The end results of a data expert's analysis should be easy enough for everyone involved to understand.

The Data Scientist's approach to data analysis depends on their industry and the specific needs of the business or department they work for. Before a Data Scientist can make sense of structured or unstructured data, business leaders and department managers must communicate what they're looking for. As such, the Data Scientist must have sufficient expertise in the business domain to translate company or department goals into data-driven deliverables such as prediction engines, pattern detection analysis, optimization algorithms, etc.

It's a growing profession - according to Indeed , job vacancies for Data Scientists increased 75% from January 2015 to January 2018.

Responsibilities of the Data Scientist 

The main responsibility of the data scientist is data analysis, a process that begins with data collection and ends with decisions made based on the final results of data analysis.

The data analyzed by Data Scientists, often referred to as big data, comes from a number of sources. Two types of data are grouped together in Big Data: structured data and unstructured data. Structured data is organized, usually in categories, which makes it easy to sort, read and organize automatically on the computer. This includes data collected by services, products and electronic devices, but rarely data collected from human data. Website traffic data, sales figures, bank accounts or GPS coordinates collected by your smartphone - these are forms of structured data.

Unstructured data, the fastest growing form of big data, is more likely to come from human inputs - customer reviews, emails, videos, social media posts, and more. This data is generally more difficult to sort and less efficient to manage with technology. Because it is not streamlined, managing unstructured data can require a significant investment. Businesses typically use keywords to interpret unstructured data in order to extract relevant data using searchable terms.

Typically, organizations hire data scientists to manage this unstructured data, while other IT staff are responsible for managing and maintaining structured data. Yes, Data Scientists are likely to process a lot of structured data over the course of their careers, but companies increasingly want to leverage unstructured data to serve their revenue goals, making unstructured data approaches essential to the role of the scientist. of data.

Data Scientist skills 

  • Programming : The most fundamental skill of a data scientist, noting that this adds value to data science skills. Programming improves your statistical skills, helps you "analyze large data sets" and allows you to create your own tools.
  • Quantitative analysis: an important skill for the analysis of large data sets. Quantitative analysis will improve your ability to run experimental analyzes, scale your data strategy, and help you implement machine learning.
  • Communication : Perhaps the most important soft skills in any industry, strong communication skills will help you harness all of the skills listed above.
  • Teamwork : Just like communication, teamwork is essential to a successful career in data. It requires being selfless, accepting feedback, and sharing your knowledge with your team.

Data Science is arguably the most revolutionary career of the 21st century. In today's high-tech world, everyone has pressing questions that “Big Data” must answer. There is an endless amount of information that can be sorted, interpreted and used for a variety of purposes. Finding the right answers can be a big challenge, however. Data Scientists are scientists hired to meet this challenge. But then what are the skills of the Data Scientist?

Because there is simply too much information for the average person to process and use, Data Scientists have the skills to gather, organize and analyze data, thereby helping people from all walks of life in the industry and of all segments of the population.

The ESSENTIAL skills that every Data Scientist must have are as follows: 

1. Training:

Data Scientists are highly skilled - 88% have at least a master's degree and 46% have a doctorate - and although there are notable exceptions, very extensive training is usually required to develop the knowledge necessary to become Data. Scientist. To become a Data Scientist, you can earn a master's degree in computer science, mathematics, statistics, or physical science. The most common fields of study are mathematics and statistics (32%), followed by computer science (19%) and other scientific fields (16%).

A degree in one of these streams will give you the skills you need to process and analyze big data.

After your program of study, you are not yet finished. The truth is, most Data Scientists have a master's or doctorate degree and also undertake online training to learn a particular skill such as using Hadoop or Big Data. The skills you learned during your degree will make it easy for you to transition into data science.

Besides learning in the classroom, you can put into practice what you have learned by creating an app or exploring data analytics for you to learn more.

2. R programming

In-depth knowledge of at least one of the analysis tools. For Data Science, R is generally preferred. R is specially designed for data science needs. You can use R to resolve any computer problem. In fact, 43% of data scientists use R to solve statistical problems. What makes R one of the most important skills of the Data Scientist. However, R has a steep learning curve.

It is difficult to learn especially if you are already proficient in a programming language. Nevertheless, there are excellent resources on the Internet to help you get started with R . You can follow the Free video training that I prepared:  R Pour La Data Science

3. Python

Python is the most common coding language that I generally consider necessary to be a Data Scientist.

Python is a great programming language for scientists. This is why the majority of Data Scientists use Python as their primary programming language.

Because of its versatility, you can use Python for almost any step involved in data science processes. It can take different data formats and you can easily import SQL tables into your code. It lets you create datasets and you can find literally any kind of dataset you need on Google. Python is therefore, without a doubt, one of the most important skills of the Data Scientist.

4. SQL database

Even though NoSQL and Hadoop have become an important component of Data Science, it is still essential that a Data Scientist must be able to write and execute complex queries in SQL.

SQL is a programming language that can help you perform operations such as adding, removing, and extracting data from a database. It can also help you perform analysis functions and transform database structures.

Indeed, SQL is specially designed to help you access, communicate and use data. It gives you an idea when you use it to query a database. It contains concise commands that can help you save time and reduce the time it takes programming to perform difficult queries. Learning SQL will help you better understand relational databases and improve your profile as a Data Scientist.

5. Machine Learning and AI.

A large number of Data Scientists do not master the fields and techniques of Machine Learning . This includes neural networks, reinforcement learning, opposition learning, etc. If you want to stand out from other data scientists, you need to know about machine learning techniques like supervised machine learning, decision trees, logistic regression, etc. These skills will help you: Solve different data science problems based on predicting key organizational outcomes.

Data Science requires the application of skills in different areas of machine learning. In one of his surveys, Kaggle found that a small percentage of data professionals mastered advanced machine learning skills such as supervised machine learning, unsupervised machine learning, time series, language processing. naturalness, outlier detection, computer vision, recommendation engines, survival. analysis, reinforcement learning and adversary learning.



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