Career transition is not easy, no matter what sector you belong to transitioning your career from one segment to another will always be a journey paved with hardships, difficult decisions to take and will involve lot of research, time and effort. And if you are looking to transition your career to data science, then you must up your game.
You need to investigate the best online courses of data science, what data science certification courses are available, and where to start your journey of transition to a career in data science.
This transition though may seem challenging, but it will be rewarding and fulfilling. The job of a data scientist is continuously evolving and there is rising need of data scientist.
Online courses are a great boon if you are looking for a data science course for beginners or want a data science certification course or for courses in data science training. The internet will be your best ally, by helping you access these courses.
What is data science and what does it mean to have a job as a data scientist?
Simply put, data science is the incorporation of domain knowledge, statistics, and computer science.
Domain knowledge is the hardest to achieve as it takes time, effort and research to gain, the other two can be perfected by studying and practicing.
Job as a data scientist starts with reporting activities to building and executing advanced and complex modules that add value to a business. The market size in 2025 is expected to reach $140 billion. This means we can expect this surge in demand for data scientists to continue abated for years ( https://www.analyticsvidhya.com/data-science-career-transition/).
Online courses for data science and data science certification courses will help you in your way to transitioning your career into a data scientist.
Hero Vired offers comprehensive job-focused data science courses that include data science for beginners. These programs cover all aspects of data science programming languages that you will need to master for success in the filed. The all inclusive data science training programs are built by expert practitioners with decades of experience in the industry working on complex and real-world business problems.
Jobs and roles in data science
Data science is a vast field and its applications are far and wide. Today there is no dearth of data, it is up to us how to leverage this data and put it to practical use.
Data science is a boon to businesses as it helps them capture larger market share, analyze the trends, help minimize the risk factor and leveraging businesses to enter new markets and deliver innovative products.
Due to the expanse and size of the domain, sometimes it becomes a bit ambiguous as to what is a data scientist and what are the roles that are offered and acknowledged in this field.
The roles that are highly acknowledged in the industry include:
- Data Scientist
- Data Engineer
- Data Analyst
- Business Analyst
- Data and Analytics Manager
- Database Administrator
- Data Architect
As we are focusing on the role of a data scientist lets explore what does a Data Scientist role look like and what are the skills needed for you to transition successfully to a job as a data scientist.
Let’s also take a look at how you can gain better and deeper understanding of this role through structured data science training and learning programs.
As a Data Scientist you will need to:
- Understand the problem statement
- Gather data
- Clean the data
- Build data models
- Deploy the solution
- Conduct exploratory data analysis
Skills every Data Scientist needs
We will simplify and break down the major skills you need to work on, if you are planning to transition your career to data science.
Online courses such as data programming languages, data science for beginners , data science certification courses and data science courses are some that you can explore to transition your career smoothly .
The skill set is divided in two major categories:
Below are the technical skills you need to be a successful Data Scientist:
|Statistics & Mathematics
|Descriptive Statistics, Inferential Statistics, Linear Algebra, Differential Calculus, Discrete Mathematics
|Getting Data In/Out, Managing Data frames, Loop Functions, Regular Expressions, Control Structures, Implementing Machine Learning algorithms
|Big Data/Data Engineering
|Hadoop Ecosystem (Hive, Pig, Sqoop, Flume), Big Data Lakes, No SQL, Apache Spark, Spark MLLib
|SQL, Microsoft Power BI, SAP BI, Tableau, Oracle Fusion
|Scikit-Learn: Regression, Classification, Segmentation, Feature Engineering, Dimensionality Reduction, Training and Deploying Models
|Advanced Machine Learning (Deep Learning)
|TensorFlow, Keras, Artificial Neural Networks, Deep NeuralNets, Convolutional Neural Networks, Autoencoders, Reinforcement Learning
|Solid understanding of the industry you’re working in and know what business problems your company is trying to solve. You must understand how the problem you solve can impact the business
Apart from the above-mentioned skills you need to master tools such as
- Microsoft excel
- Tableau Software
The soft skills include having:
- Structed, logical and analytical thinking skills
- Problem solving skills
- Business and industry acumen
- Looking beyond the average
You now have an overview of the roles, responsibilities and skills required for your job as data scientists.
Tips for a smooth data science career transition
Delve into the data science world and explore
As you have decided to transition your career into the field of a data scientist, explore this world, understand the nuances and intricacies of what it entails to be a data scientist.
What are the roles being offered in the market, what does the salary look like, what is the future scope and what you need to work on to be able to take up a job of a data scientist?
Understanding where your passion lies in, will empower you to be comfortable to step out of your comfort zone. So, dig as deep, take up courses in data science, data certification, data science training and data science programming languages to give you the required leverage.
Mentoring and networking
In order to make a smooth hassle-free transition, talk to industry experts, network with professionals already in the segment. They will be able to give you a clearer picture, mentor you in the right direction and guide you by making sure you’re not diverging from your goal.
Transitioning to a data scientist can become overwhelming at times , in order to help you always try to get a mentor or talk to someone from the industry.
Not only will they help alleviate your fears, but they will also give you honest feedback about where and what you need to work on. They will also guide you on what courses you need to take up in order to successfully transition to a career in data science.
Just taking up online courses in data science is not going to give you the outcome you are looking for. You need to understand where you stand in terms of your skills, what skills you need to improvise on and which are the skills best suited for you to accomplish this goal.
Hence its important you choose the right online course be it in data science, data certification, or data science courses for beginners. As quality learning will give you the right foundation on which to build and succeed as a data scientist.
Learning theoretical data will only take you so far. You Should be able to practically apply the concepts. You need associate or involve your self in real time projects.
You can do this by taking part in various competitions and challenges. The benefit of this is you will understand your strengths and focus areas you need to work on.
It will also give you a clear picture on how well you have understood the concepts and what it means to implement this practically and in real life.
Communication an essential arsenal in your skill set, in order to convey your message or ensure recruiters hire you, you need to communicate well with them.
Having knowledge about data science is not enough, you need to constantly work on communication skills, to ensure people listen to you and you are able to convey to them that you have good communication to deal with all stakeholders across the board.
Common mistakes to avoid while transitioning to a career in data science
- Learning theoretical concepts without applying them
- Relying solely on certifications and degrees
- Focusing on model accuracy over applicability and interpretability in the domain
- Using too many data science terms in your resume
- Giving tools and libraries precedence over the business problem
- Not spending enough time on exploring and visualizing the data
Data science is still growing as a function and practice. It is going to bloom and grow even more and if you are thinking of a career transition, then this is the right time.
Business and organizations are going to need data scientist who can help break down complex data, draw conclusions and help support them in the decision-making process.
Understand what the job as a data scientist entails, look into certification courses for data scientist and data scientist programming language courses. Go into with your eyes wide open and understand that hard work, passion and perseverance is going to go a long way in helping you achieve your dream job.
This journey is going to be tough but not impossible and the results are going to more than what you expected. And to get started on this journey, the Hero Vired Integrated Program in Data Science, Machine Learning, and Artificial Intelligence will be a perfect fit for you if you’re looking to learn from an intensive program that covers all aspects of data science.
The program is offered in collaboration with MIT and integrates with the MIT MicroMasters® program, allowing you access to leading and world-class curriculum from one of the top universities in the world.
It also includes over 80 live classes with highly-experienced industry faculty and mentors, along with hands-on case studies, assignments, and a capstone project.
Not only that, the data science program offers Placement Assurance, meaning you will be able to make the successful transition from the program to a job in data science.