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In my previous blog how to choose a data science program , I discussed a bit about choosing a Data Science Program. It takes some time and energy and resources before one aquires necessary knowledge, competence and skills to become a Data Scientist or anything in general.
Data Analyst, Data Engineer, Machine Learning Engineer, BI Analyst, System Analyst, Big Data Engineer or Data Scientist, there are multiple jobs titles to start from, based on your existing levels of experience, interest, competence. The key is to learn at your own comfortable pace under the right guidance, without crusing your sprits. People might say, you're crazy to choose this path. Don't let the Imposter Syndrome override you. Training combined with the practical skills, shall take your there.
Be you are fresher or a working professional, or someone who just feels stuck in the rut, everybody got to think what they want to achieve. At Vyom Data Science`s, we help you realize your aspirations in a structured and timely manner upto your satisfaction.
The demand for Data Science professionals is growing at an exponential rate.
The Big Three: Data Analyst, Data Scientist, and Data Engineer
Well, There’s no universal definition of “data scientist” or “data analyst” that every company agrees on, so different positions with the same title may require different skill sets. There are a plethora of other commonly-used job titles that involve data science work that you might not find if you’re just searching for “data analyst” or “data scientist” roles.
Now, probably your challenge lies in choosing which ones to take and to prioritize. The choices can get overwhelming and will give you that urgent feeling that you need to take on as many courses as you can to become an expert in the fastest way possible.
What is a data analyst? This is typically considered an “entry-level” position in the data science field, although not all data analysts are junior and salaries can range widely.
Skills required: Specifics vary from position to position, but in general, if you’re looking for data analyst roles, you’ll want to be comfortable with:
- Intermediate data science programming in Python, including the use of popular packages
- Intermediate SQL queries
- Data cleaning
- Data visualization
- Probability and statistics
- Communicating complex data analysis clearly and understandably to people with no statistics or programming background
What is a data scientist? Data scientists do many of the same things as data analysts, but they also typically build machine learning models to make accurate predictions about the future based on past data. A data scientist often has more freedom to pursue their own ideas and experiment to find interesting patterns and trends in the data that management may not have thought about.
Skills required: All of the skills required of a data analyst, plus:
- A solid understanding of both supervised and unsupervised machine learning methods
- A strong understanding of statistics and the ability to evaluate statistical models
- More advanced data-science-related programming skills in Python or R, and potentially familiarity with other tools like Apache Spark
What is a data engineer? A data engineer manages a company’s data infrastructure. Their job requires a lot less statistical analysis and a lot more software development and programming skill.
Skills required: The skills required for data engineer positions tend to be more focused on software development. Depending on the company you’re looking at, they may also be quite dependent on familiarity with specific technologies that are already part of the company’s stack. But in general, a data engineer needs:
- Advanced programming skills (probably in Python) for working with large datasets and building data pipelines
- Advanced SQL skills and probably familiarity with a system like Postgres
Step 1- The Math
The main topics concerning mathematics that you should familiarize yourself with if you want to go into data science are probability, statistics, and linear algebra. As you learn more about other topics such as statistical learning (machine learning) these core mathematical foundations will serve as a base for your learning.
- Probability: Probability is the measure of the likelihood that an event will occur. A lot of data science is based on attempting to measure the likelihood of events, everything from the odds of an advertisement getting clicked on, to the probability of failure for a part on an assembly line.
- Statistics: Once you have a firm grasp on probability theory you can move on to learning about statistics, which is the general branch of mathematics that deals with analyzing and interpreting data.
- Linear Algebra: It covers the study of vector spacing and linear mapping between these spaces. It is used heavily in machine learning, and if you really want to understand how these algorithms work, you will need to build a basic understanding of Linear Algebra.
Step 2- The Key Technologies
The data science community has mainly adopted Hadoop, Cloud Computing, Machine learning and Python as its key technologies. Let me give you a brief of it:
- Learn to Love (Big) Data - Data scientists handle a humungous volume of segregated and non-segregated data on which computations often cannot be performed using a single machine. Most of them use big data software like Hadoop, MapReduce, or Spark to achieve distributed processing.
- Gain a Thorough Knowledge of Databases - Given the huge amount of data generated virtually every minute, most industries employ database management software such MySQL, HBase or Cassandra to store and analyze data. Good insight of the workings of the DBMS will surely go a long way in securing your dream job as a data scientist.
- Cloud Computing - Another buzz word in the industry! In its layman form, it is just a computer, somewhere else. The data is so huge that moving the data from A to B is simply not an option anymore due the the resources involved.
- Python - Python is an interpreted, high-level programming language. Python allows programmers to use different programming styles to create simple or complex programs, get quicker results and write code almost as if speaking in a human language. Companies are looking for this language specifically.
- Machine Learning - Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- For Visualization, Tableau- Tableau is the most powerful, secure, and flexible end-to-end analytics platform for your data. Tableau is the only business intelligence platform that turns your data into insights that drive action. That actually very helpful if you learn and then go for best opportunities because that is the must element companies will look forward from your side.
Step 3- The Community
The job search for data scientist positions can take a while, its best to begin building out your network!
One of the best ways to begin to build out your network is to attend meetups for data science! But you don’t need to be limited strictly to data science, you should attend meetups with any topics that are related to data science, things like Python meetups, Visualization meetups, etc.
Step 4- The Job Search
One of the most realistic ways ever to become a data scientist is to work as a data scientist. Nothing can ever surpass the experience you gain from doing real-life projects.
At Vyom Data Science`s, we provide 100% interview guarantee after successful completion of the course.
Getting industry exposure to enhance your skill as a data scientist is the key. Start an internship or join a boot camp or if you already have experience as an Analyst, then get bigger and better projects to become an expert in Industry. And there always is the chance to convert an internship to a full-time job!