How to Become A Data Scientist

data scientist

Ever wondered what it takes to become a data scientist? Well, becoming a data scientist is actually the dream of many young people in our century. The truth however is that becoming a successful data scientist is not as easy as it sounds in theory. There is definitely more to the title than most of the people would imagine. The field of data science and the interest of people and companies in data and the ability to leverage information to increase profits and improve operations only continues to grow. Not just in the private sector either, data science is utilized heavily in the public space when it comes to public health, housing, and almost anything you can think of. Everyone from large social media conglomerates to adult entertainment companies are looking to capitalize on data they obtain. For example, a well known adult hookup app known as MNF App gained new users for their meet and fuck games by analyzing data from other large adult dating companies in order to target people that were more likely to seek casual sex and new fuck buddies. By piggybacking off of the success of other adult dating platforms they were able to become direct competitors with them. This is just one example of how companies and industries you might not expect are utilizing data and data science to their advantage.

All this to say that data scientists are in high demand and the demand will only continue to grow as more and more people, companies, and organizations look to capitalize on information. The following are the steps through which one gets to thrive and at the same time stand out in the field of data science.

Personal Evaluation

This ought to start even before one makes any huge financial investments in the area such as going to school. One could familiarize themselves with data science related stuff. For instance, perusing through a number of educational materials from the data science field, getting acquainted to some of the tasks they perform, or even getting to interact with a number of people in that field. This gives one a feeling on what it feels like to be a data scientist. This will go a long way to help one decide whether this is indeed the path they would like to take.

Work On the Academic Qualifications Required

Kick off on the basic education path towards becoming a data scientist. This involves choosing the right school to attend for an undergraduate degree. This is the starting point of the amazing journey. The education path may also cover even the masters’ period. It is at this point where one is ready to choose an area of specialization. The catch with this approach is that one gets to focus their energy and efforts to that particular area. This will enable one to learn as much as they can and be the best in a particular area of focus. Remember it is not just about becoming like any other data scientist, but becoming a great data scientist.

Put Your Education To Work

This only means get out and exercise the skills learnt at school. At this point, one needs to go for the jobs that fall within their area of expertise as well as their qualifications. This way, one is able to work on an area that they are conversant with. This enables one give their best and boosts their morale. Keep working and loving what you do. This is the best way, to keep increasing the experience and knowledge in a particular field of choice. At the same time, one gets to build their name and portfolio while boosting their skills in the area.

Here is a great video that sums up and adds to some of the points already discussed:

Keep Learning

In as much as one is done with the academics bit, learning never stops. Everyday something new is comes up. Keep learning through books, the internet as well as from other data scientists. This will keep one at par with the emerging trends in their area of specialization. It further gives an upper hand when it comes to meeting the needs of the industry as one is well informed. All of these make one well equipped to handle tasks hand over to them while at the course of duty.

What is Data Science?

We all know that this is the time of big data. And from the processing of this data to the storage of this data, all are very important for us. Before the introduction of different frameworks like Hadoop, data storage was a big concern. However, it has been resolved now with the help of different types of frameworks. Data Science is somewhere responsible and helpful for both storage and processing of the data. You will see many people around, who are trying to become a Data Scientist. They are also trying to gain knowledge related to different types of frameworks related to Data Science. But before one start learning about data science or its framework, they should try to know about the data science and why it is required or why it is important. Here, we will read about all this.

Data Science

To discover the raw data’s hidden patterns, different types of machine learning principles, algorithms, and different tools are used. This blend of all of these algorithms and principles which is used for this purpose is known as Data Science. Data Science uses different techniques like machine learning, prescriptive analytics, and predictive causal analytics to make predictions and decisions. A data scientist needs to analyze and look at the data from different angles.

For helping the organization make better decisions, the data scientists try to uncover and gather all the findings from the data that is present in the repository of the organization. To find out the meaningful trends, insights, and inferences, from the unstructured raw data, data science is required. The information and findings that are gathered are then processed using business skills and analytical skills, and programming. In the past few years, the data science has evolved and it continues to evolve to provide organizations with success using proper information, better predictions, and right decisions. Earlier, the need for a data scientist was not discovered and no one took it very seriously. But slowly and gradually with the time, the organizations started realizing that they do have the need for the data science skilled professionals who can handle the big amount of data and can organize it properly. There is no organization which is not working with facts & figures or data. To mine or to interpret or to analyse the required information from the complex data is what data science is all about.

Importance of Data Science in the organizations and businesses

In this competitive world, where most of the organizations are giving tough competition to each other, it is very important to make use of Data Science. The organizations need better predictions and better decisions, and for that, a data scientist is very important to evaluate and analyse the organization’s data. There are different ways in which data science is important for organizations and businesses. Let’s know the importance of Data Science.

Helps in defining the organization’s goals

As a data scientist, one tries to examine the data of the organization. After examining the data properly, the data scientists recommend certain actions and steps which are important for the organization. Based on the data trend, the data scientists help in defining the goals of the organization for helping the organization to improve its performance and profitability. When we say an organization’s goals, we did not just mean the one certain goal. But we also mean that it helps various departments by letting them know their individual goals also which will contribute to the profitability of the organization.

Helps to identify the opportunities

The data scientists do not just examine the data. But they also question the assumptions and the processes that are being used for the development of the organization. Be it the questioning about the development of the analytical algorithms or any other such tools or methods, the data scientists check them carefully. They try to find out new opportunities and ways to improve organizational value.

Helps in finding the right talent

We know that recruiting the new talents is the job of the recruiters. But data scientists also help in recruiting by checking the data available about the candidate on the website. They gather information through corporate databases, social media sites, and job portals. They work on this data to find out the best talent for the organization.

3 types of Data Formats Explained

Data appears in different sizes and shapes, it can be numerical data, text, multimedia, research data, or a few other types of data. The data format is said to be a kind of format which is used for coding the data. The data is coded in different ways. It is being coded, so that it can be read, recognized, and used by the different applications and programs.

In the information technology, the data format may be referred in different ways. It can be termed as the data type, which is a constraint in the type system which was positioned after the interpretation of the data. It is also termed as a file format which is being used for storing the encoding data in a computer file. Or it can also be termed as Content Format, where the media data is represented in the particular format, that is a video format and audio format.

When it comes to choosing a data format, there are several things which one need to check like the characteristics of the data or the size of the data, infrastructure of the projects, and the use case scenarios. Certain tests are performed in order to choose the right data format by checking the speed of writing and reading the data file. Mainly there are three main types of data formats which are also called as GIS Data formats. All of these data formats are handled in a different way. They are being used for different purposes. The three data formats are: 

  • File-Based Data Format
  • Directory-Based Data Format
  • Database Connections 

Below, we have explained these three types of data formats:

File-Based Data Format – This type of data format includes either one file or more than one file. These files are then stored in any of the arbitrary folders. In most of the cases, it uses the single file only for example DGN. But then there are cases, which even includes at least three files. The filename extension of all these three files is different from each other. That is SHX, SHP, and DBF. All three files are important and are required here. As different tasks are performed by all these three files internally. One uses the filename as the name of the data source. There are many layers present in it, and it is not possible to know about them just with the help of the filename. Like in shapefile, there is only one data source for every shapefile. And there is only one layer, which is named similarly as the name of the file. Some of the examples of file-based data format are Microstation Design Files, Shapefiles, and GeoTIFF images.

Directory-Based Data Format – In this type of data format, whether there is one file or there is more than one file, they are all stored in the parent folder in a particular manner. There are some cases where the requirement of an additional folder is there in the file tree in some other location so that it can be accessed easily. It is a possibility that data source is the directory itself. There are many files present in the directory, which are represented at the available data’s layers. For example, the Polygon Data is represented by the PAL.ADF. As there is more than one file in the folder with the ADF file extension which is included in the ESRI ArcInfo Coverages. The ADF file extension includes the line string data or the arc string data. All the ADF files serve as a layer which is present in the data source inside the folder. Some of the examples of Directory-Based Data Format are US Census TIGER and ESRI ArcInfo Coverages.

Database Connections – In one respect, the database connections are quite similar to the above-mentioned data formats that are file and directory-based data format. For interpreting, for MapServer, they give geographic coordinate data. One need to access the coordinates inside the MapServer, that are creating the vector datasets. The stream of coordinates that are provided by the database connections is stored temporarily in the memory. The MapServer then reads these coordinates for making the map. Coordinate Data is the most important part and most of the focus is on it only. However, one may also require tabular data and attributes. The database connection generally consists of the following information like Host that is the server’s direction, Database Name, the Username and Passwords, Geographic Column name, and the table name or the view name. A few examples of Database Connections are MySQL, ESRI, PostGIS, and ArcSDE.

Benefits of data format types

With data format types being in place, it becomes easy for the user to carry out multiple operations and make the most of it. some of the benefits of having data format types has been listed below:

  • Calculations: Calculations have never been easy before the introduction of data format types. With these formats, all you have to do is punch in the values and within no time all the calculation is done and at your disposal.
  • Formatted: The data if kept well formatted and organized is presentable and understandable by the users. Thus individuals referring to such data can make the most of it. if a user has to make a similar presentation at different points of time, they can simply pick up a format and keep using it for drafting presentations.
  • Consistency: Data types helps the user to have variable that is consistent throughout the program. So you can simply rely on the variable to make presentations, or calculations.
  • Readable: The data is readable and accessible to users all the time without any hassle. Hence any job can be done at the earliest with maximum output produced.


From the facts mentioned above, it is well evident to the users that what are different data format types, its benefits and how these can be used for producing more efficiency and results. Companies are leaning on data more and more in order to improve sales and efficiency. This includes agriculture companies, social media, brands, and even adult dating companies. When backpage went down any true competitors needed to analyze the data in order to offer a true backpage alternative site. Of course many dating services have tried, but it has yet to truly be accomplished. This is just one example of the value of data to various companies in various industries. Individuals can even approach professionals in the field to gain complete knowledge about data type formats and make the most out of it.