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Tag: How to become a data Scientist?

Why Does Data Ops For Data Science Project Matter?

Posted on by Imarticus Learning

What is Data Science?

Data plays a major role in every organization as it helps in making decisions based on facts, statistics, and trends. Data science helps to trace insights from the raw data generated, which in turn is used to make major business decisions. Implementing Data Science in business has several advantages.

  • It helps in reducing risks and identifying fraud models. Data scientists are trained to identify data that stands out in some way and they use methodologies to predict fraud models along with creating alerts every time unusual data is identified.
  • It helps organizations in identifying when and where the products best sell. This helps the organization to deliver the right products at the right time as per the customers’ needs.
  • It helps the sales and marketing teams to understand their audience well and helps with providing personalized customer experiences.

Why Data Science Needs DataOps?

Data scientists deal with searching for data, labeling, cleaning, and performing other tasks that consume a lot of time. Especially if the business has to maintain a backlog legacy, then the amount of data keeps multiplying every year. This is where the need for DataOps rises.

DataOps involves collaboration, automation, and continuous innovation to data within a data-driven environment. Just like software can not be expected to provide exact results outside its live environment, data projects may also tend to behave similarly and may have to be reworked completely to make it work in a production environment. It also has to be continuously monitored even after deployment. Which makes it even more necessary to implement DataOps in a Data Science project.

Data Ops for Data ScienceDataOps plays a major role in building best practices throughout a function. Through continuous production, DataOps helps organizations to deliver value to a range of stakeholders.

Another significance of using DataOps in Data Science is Automation. Data moves through a particular process within an organization. While Data is entered in one form, it does not exist in the same form. Data scientists have to build data pipelines, test, and change them before data is deployed.

Making use of DataOps best practices, you can get a constant stream of data flowing through the pipelines. Which in turn, helps to attain real-time insights from the data. This ensures to reduce the time taken in converting raw data into Valuable information.

Combining Machine Learning with DataOps helps in maintaining a continuous workflow through internal communication. With this, the data quality can be controlled through version control, constant development, and integration. Combining ML also improves the insights and has a great potential for extracting value from DataOps.

Introducing DataOps in the organization also means changes in the work process. It builds a new ecosystem with consistent communication between the departments. Employees of each department work together, in real-time, sharing a common goal.

Therefore, using DataOps in Data Science ensures to develop projects keeping in mind the business impact along with delivering it in a way that the management can understand.

Why Data Science Course?

The Data Science course covers a mix of topics like mathematics, Tools, Machine Learning techniques, Business Acumen, and several algorithms. The main principle behind Data Science is finding patterns from gigabytes of raw data collected.

In today’s competitive world, more and more organizations are opening up to big data, and the need for data scientists is also on the rise. They get exciting opportunities to work on and also get to come up with solutions for businesses.

10 Data Science Careers That Are Shaping the Future!

Posted on by Imarticus Learning

Data is wealth in modern days and data scientists will be in huge demand in the coming years. Firms require skilled professionals to analyze the generated data. Data analysis is also predicted to surge with the rise of new-age technologies like machine learning, artificial intelligence, etc.

According to reports, there is a shortage of expert data scientists in the market. One can opt for a post-graduate program in machine learning to gain the skills needed in the data science industry.

Let us see about ten data science careers that are shaping the future.

Data Scientist

Data Scientists have to organize the raw data and then analyze it to create better business strategies. Data is analyzed for predicting trends, forecasting, etc.

Data science careerData scientists are technical personals who are fluent in data analysis software and use them to predict market patterns. Firms will require more skilled data scientists in the future due to the need to process & analyze big data.

Business Intelligence Analyst

Business Intelligence (BI) analysts & developers are required to create better business models. They also help in making better business decisions. Policy formation and strategy development are key responsibilities of a BI analyst. Firms have to face market disruptions and need good business models/strategies to tackle them. BI analyst/developer will be in demand in the coming days.

Machine learning Engineer

Machine Learning (ML) Engineers are required for creating better data analysis algorithms. They have research about new data approaches that can be used in adaptive systems. ML engineers often use other technologies like deep learning, artificial intelligence, etc. to create automation in data analysis.

Applications Architect

Firms require good applications and user interfaces to run business processes smoothly. Applications architects choose or create the right application for their firms. Due to the rise in the complexity of data, firms will require better applications to manage it.

Statistics Analyst

A Statistics analyst or statistician is required to interpret the data and present it in an understandable way to non-technicians. They have to highlight the key insights in big data to stakeholders/fellow employees. Data analysis results are also used to make predictions and identify potential opportunities. You need to be good with numerology if you are thinking to become a statistician.

Data Analyst

They have to convert large data sets into a suitable format for data analysis. They also help in finding the data outliers which can affect the business. There is a lot of data generated every day as humans analyze less than 0.5 percent of data produced! Data analysts are already in huge demand in the data science industry.

Infrastructure Architect

Infrastructure architect in a firm makes sure that the applications, software(s), databases used by the firm are efficient. Infrastructure architects also help in cost optimization. They make sure that their firm has the necessary tools for analyzing big data.

Data Architect

Data architects mainly focus on maintaining databases.

Data Science CareerThey attempt to make the database framework better. With the rise of automation in data science, data architects are in huge demand to provide better solutions.

Enterprise Architect

Enterprise architects are IT experts and provide firms with better IT architecture models. They suggest stakeholders & senior managers in choosing the right IT applications for data analysis. Top companies like Microsoft, Cisco, etc. hire enterprise architects for maintaining their IT framework.

Data Engineer

Data engineers are required to create a good data ecosystem for their firms where the data pipelines are maintained. Data Engineers are required to choose better data analysis applications to provide real-time processing. They also help in making the data available to data scientists.

Conclusion

Data science is a growing field and there are a lot of job opportunities. You can learn Data Science Courses in India from a reliable source like Imarticus learning. One can also target any particular job role in the data science industry and should learn the necessary skills. Start your post-graduate program in machine learning now!

SQL For Data Science: One-Stop Solution For Beginners!

Posted on by Imarticus Learning

Data science has earned the reputation of being the most promising job of the times, even during this pandemic crisis. With the current changes in the global business and economic background, data science has proven to be a more relevant career opportunity. If you are following the subject and have a keen interest in making a data science career choice, you must have heard about SQL as well.

SQL online trainingSQL is used to access and manipulate data. It helps to store data, access whenever you need it, and retrieve if need be. SQL training will give you a much-required head start in the highly competitive job market.

Why is SQL Important in Data Science?

Today’s business decisions are data-driven. Data is generated all through the day, across the globe. The amount of data generated every day is simply astonishing – about 2.5 quintillion bytes. This underlines the enormity of the subject we are dealing with.

Now that data is available, what is the next thing? How are you going to make sense of this huge amount of data and use it to make a decision? Data science steps in here. You need to collect, organize, and process them to make sense of the data and to derive insights. To do this, you need tools.  This is what SQL does. It is a querying language used to store, access, and retrieve data.

What is Structured Query Language (SQL)

SQL is a language that is primarily concerned with managing relational databases. SQL is the typical API for such data tables. While using SQL, data can be accessed and managed without changing the databases. You can perform a variety of actions including updating, querying, deleting, and inserting data records. Oracle, MySQL etc. are examples of such databases which use SQL.

SQL works based on some simple commands that are associated with different data tasks. These commands can be used to create database and tables, insert, delete, or update data, to alter table and database, drop table and index.

How to Create a Table Using SQL

Let’s see how to create a table using SQL commands. Remember to use UPPERCASE letters for SQL commands, and use semicolons to terminate commands.

data science careerYou may follow the steps given below to create a database.

Step #1 Creating a Database using SQL

CREATE DATABASE: Use this command to create a database “Test”.

USE: This command activates the database.

CREATE test;

USE test;

Your database named test is ready and activated.

Step #2: Creating a Data Table

It is as easy as typing a command to create a table, just like the way you created the database. All you need to do is to decide on the variables you want to include in the table.

SQL online trainingSuppose you want to create a table with the following features:

  1. Serial Number (SL)
  2. Purchase item
  3. Cost
  4. Number of pieces

You can use the command CREATE TABLE to create the table. The four features of the table are SL, purchase item, cost, and number of pieces.

Now, to create the table, use the command as given below:

CREATE TABLE cart (SL NOT NULL PRIMARY KEY AUTO_INCREMENT Purchase_item TEXT, Cost INTEGER, Number_of_pieces INTEGER);

You might have noticed that we have given the value we are going to provide for each feature. The Serial Number is a primary key, which means it represents a unique data. The purchase item will be entered as text, while cost and number of pieces will be entered as numbers.

The table is now ready with the field names and the value to be entered to each cell of the table. To see how the table is executed, type the command “DESCRIBE cart”. This will give you a display of a table with the given features.

Field Type Null Key Default Extra
SL Int(11) NO PRI NULL Auto_increment
Purchase Item Text YES NULL
Cost Int(11) YES NULL
Number of pieces Int(11) YES NULL

Step #3: Data Input

Once you create the table, you need to enter data into the respective fields. To do this. Use the SQL command “INSERT INTO”.

To insert values, follow this pattern:

INSERT INTO cart VALUES (NULL, “Rice”, 75, 10)

The “null” value is assigned to SL, as it will follow the command and auto_increment from 1.

The entered value will look like:

SL Purchase item Cost Number of pieces
1 Rice 75 10

Follow the same pattern to enter more values.

Data Science is trending these days. Getting trained in a skill that is much in demand improves your chances of getting hired manifold.

So, choose a good data science course and give your profile an extra edge while competing for career opportunities.

What Does It Take To Be A Good Data Scientist?

Posted on by Imarticus Learning

What does a data scientist do?

The importance and applications of data science have grown exponentially over the past decade. Data science is still in its nascent stage and there’s a whole lot to be identified about this discipline. Businesses have started implanting strategic decision-making tools that leverage data science.

Data helps businesses by providing them with hidden insights and helps them predict the future outcome of their decision. This helps organizations to make a better business decision.

Let’s delve deeper into what these data scientists do and how it helps the organizations.

  • Finding a solution to business problems

Data ScienceOne of the most basic and key responsibilities of data scientists in an organization is to identify existing challenges and problems that a business is facing and finding solutions to remedy the situation. This might seem like a generic responsibility of every important professional but the main difference here is that data scientists use tons of relevant data to find the problem.

They try to come up with solutions after properly assessing the situation using various analytical tools that provide them with useful insights. They leverage statistical analysis, data visualization and mining techniques to provide effective solutions.

  • Find out relevant data using complex research

Data Science CareerThe 21st Century businesses are complex than ever, there are various factors that determine the fate of an organization. With the number of complexities that exist, it’s very difficult to figure out what impacts your business and how it does that.

Data scientists simplify this for organizations by studying all variables affecting a business. They use complex research work to identify the variables that have a maximum impact over the business and which are highly relevant.

  • Identify patterns and trends

Another important work of a data scientist that helps businesses is to identify patterns and trends. Data scientists use sophisticated data analysis techniques to find trends and patterns from the data sets at hand. These data sets are generally historical records of the organization. It helps them to identify the existing patterns and trends which is used to make predictions regarding the future movement of the variables.

How to become a data scientist?

Data Science CourseData science is one of the most in-demand skills in the industry and given the wide range of applications that it has, the demand for a data science professional will continue to rise in the future. One of the most common questions in the minds of data science aspirants is how to become a data scientist? There is no specific answer to this particular question. It depends on what stage of your career you are at and the skillset that you have.

A data science course by reputed institutions such as Imarticus Learning guarantees placement with top-notch firms in the industry in addition to providing relevant knowledge and skills. It also helps you provide guidance from the industry experts who are highly experienced in this domain.

Let’s delve deeper into some of the most prominent skills for data scientists that you should hone if you are planning to opt for a career in this field.

Analytical skills

One of the key skills that are required in this profession and that forms the base of all your work is your analytical skills. One should have an analytical mindset and should be able to identify trends and patterns from a big chunk of data. You should be able to assess a situation from a different perspective to reach a successful conclusion. One should be trained to work with software like Python and R and should be equipped enough to handle large volumes of data.

Problem-solving skills

Another important skill that you need to work on is your problem-solving skills. You need to use data to figure out challenges that exist in the business. After you have figured out the problems you will have to provide a solution using data analytics tools that will help the business to achieve its goals and objectives.

A Day In The Life Of A Data Scientist!

Posted on by Imarticus Learning

The data science field holds immense career potential, yet you must be thinking, what actually do data scientists do the entire day?

To provide you deep insights into data scientists’ usual tasks so you can imagine yourself in that role and decide if the time is ripe to get trained for it, we have gathered some insights for you.

No Such Typical Day

If you ask somebody working as a data scientist about their typical working day, he/she may burst into laughter after listening “typical”. For those who are adaptable and flexible, and love to do various responsibilities, then a typical day of data scientists should fit them just fine. While these workdays are subject to changes, some essence of the day stays as it is – working with people, working with data, and working to stay abreast of the field.

Data is Everywhere

Given the job role, it is no surprise that data scientists’ regular tasks hover around data. A major portion of their time is consumed in collecting data, analyzing data, processing data, yet in several ways and for several reasons. Data-centric responsibilities that data scientists may come across include:

  • Pulling, merging and assessing data
  • Searching for trends or patterns
  • Leveraging numerous tools such as Hadoop, R, MATLAB, Hive, PySpark, Python, Excel, and/or SQL
  • Developing predictive models
  • Striving to streamline data issues
  • Developing and testing new algorithms
  • Creating data visualizations
  • Gathering proofs of concepts
  • Noting down outcomes to share with colleagues

Interacting With a Broad Range of Shareholders

This may appear as if it has a minor role in data scientists’ day, yet the otherwise is true as eventually, your job is to ward off issues, not create models.

It is paramount to remember that even though data scientists are playing with data and figures, the reason for this is fueled by a business requirement. Having the ability to view the larger picture from a department’s perspective is vital. So is being able to comprehend the tactic behind the requirement, and to assist people comprehend the consequences of their decisions.

Data scientists dedicate their time in meetings and replying to emails, just like most people do in the corporate sphere. Yet, communication skills may carry greater importance for data scientists. While attending those meetings and responding to those emails, as a data scientist, you should be able to elucidate the science behind the data in layman terms, as well as able to comprehend their issues as they view them, not from data scientists’ viewpoint.

Staying Updated with Changes

Both, working with data as well as with others will account for a notable portion of the day if you decide to pursue a career in the field of data science. The remaining of your day will be captured staying updated with the data science industry. New insights arrive on a daily basis as other data scientists craft a solution to fix an issue, and then extend their new finding.

Data scientists, thus, normally dedicate a portion of the day going through industry-centric articles, newsletters, blogs, and discussion boards. They may attend conferences or connect online with various data scientists. Moreover, occasionally, they may be the ones to extend new insights.

As data scientists, you do not wish to waste time starting from scratch. If anyone else has a better solution to fix an issue, you would like to know. Staying updated with changes is the sole way you will have the ability to do that.

Now the question arises, how to become a data scientist? Well, the good news is you do not have to worry much about it. There are loads of resources available at your doorstep in the form of online courses and e-books. So, if you want to pursue a career as a data scientist, grab these resources and get yourselves enlightened.