The popularity of data science boomed in the post-2010 period. It has been there for years. Most of the people who have versatile skills have been at work with it for years. But where were they lagging? The speed of the computers was one of the areas where development was still required. The second area where they required a bit of initiative was with the type of apps that were being created. Slowly, with the help of better processors, which were also very quick and had good computation capability, we were able to make a breakthrough in AI and ML. This was extremely beneficial from the view of data science and analytics as well. Companies also understood the importance of collecting user data. They had started in the 1990s, but it was only now that we could see the results. The FAANG companies' success is living proof of that.
There's a big scope for analytics in big data as it finds many applications in this domain. According to Jeff Bezos and Mark Cuban, analytics will be a big player in the coming decade, especially in e-commerce. The increased use of machine learning in analytics systems could use the precious personal data of the customers that companies collect to give purchasing suggestions has miraculously transformed the shopping experience. Not only that, social media giants such as Facebook, and Twitter, along with various dating apps, are also utilising user data to optimise the media feed posts or potentially match people on their systems.
What are the various job roles in the field of data science?
Here are some job roles that you will be eligible for with data analyst training –
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Data Architect
The responsibility of data architects is to create a database system(s) and then maintain it for the preparation and processing of analysing data. To understand and develop the database system's architecture, they must first comprehend the process of building models. Then they have to use machine learning techniques for wrangling raw and unstructured data into a more usable form. For this, you need to know the highest level of python and database coding. An average data architect could earn anything in the range of ₹12,50,000 – ₹20,00,000 annually.
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Machine Learning Engineers
Machine learning engineers have to rely a lot on software engineering backgrounds. So, they need to know the basics of coding, data structures, algorithms, and a lot about being able to know about self-learning and unsupervised learning systems. Their main job is to make a computer learn independently from the data and the observation. They help in the development of Machine Learning models which can perform a specific action but involve the gathering of data primarily. They need to have advanced knowledge about such data interpreting models, focusing on statistical computing, data visualisation and data mining. Machine learning engineers can expect a salary of around ₹12,50,000 – ₹20,00,000 annually.
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Data Scientist
Data scientists have responsibilities like using data analytics tools to extract the information from the gathered data sets and inferring them to make better decisions. Currently, they are required in every industry, and businesses hire such professionals to make business decisions that can amplify their organisations' growth.
Data scientists also need to know about machine learning. Still, they also require visualisation tools like Tableau or various graphical libraries of Python to process the information to arrive at a decision. In India, the average salary of data scientists is somewhere between ₹8,00,000 – ₹20,00,000 annually.
Opportunity In a Changing World: Data Analytics Course with Placement
Data Analytics is one of the best careers in the present times. To tell the truth, it is highly beneficial because it teaches you two of the most important skills that you will need in the future. The first is knowing how to analyse the data and have an insight into something with its help. The second skill is coding which is so important in today's time. Beyond 2022, these two skills will pay dividends, both in terms of monetary benefits and their use in the real world. Therefore, one needs to have data analyst training if he/she is interested in building a career in a growing sector.
What do you need to study to become a data analyst?
A PG in data analytics would be the appropriate certification. This certification should cover R or Python for interpreting the data into graphs. Make sure you pick up the relevant libraries. SAS could also be helpful for statistics. Then if you want to enter the field of Big Data, Apache Hadoop could also be relevant to Data Analysts. Tableau and Excel are a must for analytics because of the various tools that come with the package.
Imarticus Learning offers the best data analytics course with placement. With the help of this course, you can now explore the world of data analysis with the help of industry experts and gain knowledge on the nits and grits of this field. Also, this PG course in data analytics and machine learning assures placement.
To sum up, hopefully, you have received an outlook on how the world of data analytics is emerging and what the future holds for this domain. Also, how completing the data analyst training can help you adjust to this changing world and supports your career in the coming years.
