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Machine Learning Job Roles

Different type of job roles in data science:

Data Engineer vs Data Analyst vs Data Scientist vs ML Engineer

Do you ever think about how tech companies like youtube, Facebook refer yo videos, article or posts; what they do with your data store in their data center. Do you know how google, youtube, Facebook show advertise of those products which you search in amazon, Flipkart or myntra a few seconds ago or how youtube shows you the same type of video on your home page or recommend you? The answer is because of artificial intelligence.

there are mainly four types of jobs to do these kinds of work.they are

1>Data Engineer

2>Data Analyst

3>Data Scientist

4>ML Engineer

Before know about the different types of job roles in data science at first know what is data science. Data science studies of data. Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Nate Silver referred to data science as a sexed-up term for statistics.

think you are an employee in a company and your manager gives you a task to make a movie recommender system or song recommender system. the whole process is done by the below steps —

i>Data gathering-In this step data is gathered from variable sources.you can gather data by hit different websites API.

ii>Data preprocessing-In this step data is processed for analysis .data are arranged in a specific order.

iii>Model Implement-Machine learning algorithms are applied in this step. Train the model by training data sets.

iv>Testin-Test the algorithm by the test data sets. Find its accuracy.

v>Optimization- Optimization is the most essential ingredient in the recipe of machine learning algorithms. It starts with defining some kind of loss function/cost function and ends with minimizing it using one or the other optimization routine.

Now Discuss different job roles in data science — -

They are software engineers who design, build, integrate data from various resources, and manage big data. Then, they write complex queries on that, make sure it is easily accessible, works smoothly, and their goal is optimizing the performance of their company’s big data ecosystem.

They might also run some ETL (Extract, Transform and Load) on top of big datasets and create big data warehouses that can be used for reporting or analysis by data scientists. Beyond that, because Data Engineers focus more on design and architecture, they are typically not expected to know any machine learning or analytics for big data.

Skills: Hadoop, MapReduce, Hive, Pig, Data streaming, NoSQL, SQL, programming.
Tools: DashDB, MySQL, MongoDB, Cassandra.

Some of the most common responsibilities for a data engineer :

Data Analysts are experienced data professionals in their organization who can query and process data, provide reports, summarize and visualize data. They have a strong understanding of how to leverage existing tools and methods to solve a problem and help people from across the company understand specific queries with ad-hoc reports and charts.

However, they are not expected to deal with analyzing big data, nor are they typically expected to have the mathematical or research background to develop new algorithms for specific problems.

Skills: Data Analysts need to have a baseline understanding of some core skills: statistics, data munging, data visualization, exploratory data analysis,
Tools: Microsoft Excel, SPSS, SPSS Modeler, SAS, SAS Miner, SQL, Microsoft Access, Tableau, SSAS.

Data Analyst Responsibilities:

Indeed, data science is not necessarily a new field per se, but it can be considered as an advanced level of data analysis that is driven and automated by machine learning and computer science. In another word, in comparison with ‘data analysts’, in addition to data analytical skills, Data Scientists are expected to have strong programming skills, and ability to design new algorithms, handle big data, with some expertise in domain knowledge.

Skills: Python, R, Scala, Apache Spark, Hadoop, machine learning, deep learning, and statistics.
Tools: Data Science Experience, Jupyter, and RStudio.

The Data Scientist is responsible for advising the business on the potential of data, to provide new insights into the business’s mission, and through the use of advanced statistical analysis, data mining, and data visualization techniques, to create solutions that enable enhanced business performance.

For example, a YouTube ML engineer might be in charge of developing the next generation YouTube recommendation algorithm and then developing an ML pipeline around it and integrating it into YouTube such that you, the user, can end up clicking that “next” button to go see that next recommended video.

Skills for ML Engineer:-

3. Data Modeling and Evaluation

4. Applying Machine Learning Algorithms and Libraries

5. Software Engineering and System Design.

Jobs related to Machine Learning are growing rapidly as companies try to get the most out of emerging technologies. The chart below depicts the relative importance of core skills for these general types of roles, with a typical Data Analyst role for comparison.

The role of the data scientist is now a buzzworthy career. It has staying power in the marketplace and provides opportunities for people who study data science to make valuable contributions to their companies and societies at large.

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Machine Learning Job Roles

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