Clarify all your doubts about the jobs related to Machine Learning and Data Science.

Data Analyst vs Data Engineer vs Data Scientist Vs ML Engineer

Data has always been vital to any kind of decision making. Today’s world runs completely on data and none of today’s organizations would survive without data-driven decision making and strategic plans. There are several roles in the industry today that deal with data because of its invaluable insights and trust. In this article, we will discuss the key differences and similarities between a data analyst, data engineer, data scientist and ML Engineer.

Job Varieties

All the above jobs are vastly divided into two major fields — Data Science and Machine Learning. The Jobs under Data Science are Data Analyst, Data Engineer, and Data Scientist. we first have to understand what are the job roles and basic need of a Data Analyst, Data Engineer, and a Data Scientist in the field of Machine Learning(ML Engineer) and Data Science.

The Basic Needs of a Data Scientist vs Data Engineer vs Data Analyst.

1. Data Analyst:

What is Data Analyst or who are they?

The process of the extraction of information from a given pool of data is called data analytics. A data analyst is a person who engages in this form of analysis. A data analyst extracts the information through several methodologies like data cleaning, data conversion, and data modeling. There are several industries where data analytics is used, such as — technology, medicine, social science, business, etc. Industries can analyze trends in the market, requirements of their clients and overview their performances with data analysis. This allows them to make careful data-driven decisions.

This means that more and more companies are looking for talented analysts to process and interpret this valuable data. By 2020, data science and analytics (DSA) job openings are predicted to grow to 2.7 million, representing a $187 billion market opportunity.

Skills Required :

Data Analyst’s basic needs

2. Data Engineer:

What is Data Engineer or who are they?

A Data Engineer is a person who specializes in preparing data for analytical usage. Data Engineering also involves the development of platforms and architectures for data processing. In other words, a data engineer develops the foundation for various data operations. A Data Engineer is responsible for designing the format for data scientists and analysts to work on. A Data Engineer needs to have a strong technical background with the ability to create and integrate APIs. They also need to understand data pipelining and performance optimization.

Data Engineers have to work with both structured and unstructured data. Therefore, they need expertise in SQL and NoSQL databases both. Data Engineers allow data scientists to carry out their data operations. Data Engineers have to deal with Big Data where they engage in numerous operations like data cleaning, management, transformation, data deduplication, etc.

Skills Required :

Most importantly, a technical bachelor’s degree along with knowledge of Python, Java or Scala. Experience with cloud platforms, a good understanding of SQL and NoSQL databases along with Big Data: Hadoop, Spark, Kafka.Knowledge of algorithms and Data Structures and understanding the basics of distributed systems is a must. Experience with Data Visualization tools like Tableau or ElasticSearch will be a big plus.

3. Data Scientist :

What is Data Scientist or who are they?

Data Science is the most trending job in the technology sector. It has quickly emerged to be crowned as the “Sexiest Job of the 21st century”. Almost everyone talks about Data Science and companies are having a sudden requirement for a greater number of data scientists. While Data Science is still in its infantile stage, it has grown to occupy almost all the sectors of industry. Every company is looking for data scientists to increase their performance and optimize their production. Data Scientist is the one who analyses and interpret complex digital data. While there are several ways to get into a data scientist’s role, the most seamless one is by acquiring enough experience and learning the various data scientist skills. These skills include advanced statistical analyses, a complete understanding of machine learning, data conditioning, etc.

When an organization needs to answer a question or solve a problem, they turn to a data scientist to gather, process and derive valuable insights from the data. Data Scientists will explore all aspects of business and develop programs to perform robust analytics.

Skills required:

No matter what type of company you’re interviewing for you’re likely going to be expected to know a statistical programming language like R or Python, and a database querying language like SQL. A good understanding of statistics is vital for data scientists. Data Wrangling, Data Visualization & Communication is incredibly important. Software Engineering will be an additional skill.

4. ML Engineer:

Machine Learning Engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without a specific direction. They feed data into the models defined by data scientists.ML Engineers study and transform Data Science prototypes, design machine learning systems, research and implement appropriate ML algorithms and tools. They also perform statistical analysis and fine-tuning using test results, extend existing ML libraries and Frameworks.

Skills required:

Always the middle person

The common skills between a Data Scientist and ML Engineer include the applications of programming languages, statistics, data cleaning and visualization, neural networks, big data processing frameworks.

summing up,

Data Analyst Vs Data Engineer Vs Data Scientist — Responsibilities

Following are the main responsibilities of a Data Analyst –

• Analyzing the data through descriptive statistics.
• Using database query languages to retrieve and manipulate information.
• Perform data filtering, cleaning and early-stage transformation.
• Communicating results with the team using data visualization.
• Work with the management team to understand business requirements.

A Data Engineer is supposed to have the following responsibilities –

• Development, construction, and maintenance of data architectures.
• Conducting testing on large scale data platforms.
• Handling error logs and building robust data pipelines.
• Ability to handle raw and unstructured data.
• Provide recommendations for data improvement, quality, and efficiency of data.
• Ensure and support the data architecture utilized by data scientists and analysts.
• Development of data processes for data modeling, mining, and data production.

A Data Scientist is required to perform responsibilities –

• Performing data preprocessing that involves data transformation as well as data cleaning.
• Using various machine learning tools to forecast and classify patterns in the data.
• Increasing the performance and accuracy of machine learning algorithms through fine-tuning and further performance optimization.
• Understanding the requirements of the company and formulating questions that need to be addressed.
• Using robust storytelling tools to communicate results with the team members.

An ML Engineer is required to perform responsibilities –

The basic responsibility of an ML Engineer is to be the middle man between the System and the data thus collected.

Now as we know all the roles of the jobs individually we see that this is a chain of work in which one is always dependent on one another but also different in a few ways. So, to select the perfect place for yourself in this chain, one must acquire all the skills thus needed and interest the individual.