Data science! It is perhaps the most popular search term that evolved in recent years. And for good reason, considering the fact that it has wonderful career opportunities and is a highly-rewarding profession.
If you have decided to be a data scientist then this blog might equip you with some basic knowledge you will need to make the right steps in your career progression
Also, If you are a novice and want to explore career opportunities in this field then this part is also covered in depth.
So, keep reading as this article contains everything which is important to note for making a career in data science. From novice to experienced there is something for everyone here.
Keeping it quite simple and straight there are 9 segments given below and these segments literally contain all you need to know about making a career in data science.
Now, let’s get started but before anything else, let’s clarify the basic questions, “who is a data scientist? and what is data science?”
The way to go for making a career in Data Science
What is data science?
Data science can be a daunting subject. Many people will tell you that you can’t be a data scientist unless you know how to do the following:
Statistics, linear algebra, calculus, programming, databases, distributed computing, machine learning, visualization, experimental design, clustering, deep learning, natural language processing, and so on… The list is endless.
So, what is data science, exactly?
It’s a process of posing intriguing questions and then using data to answer them.
The data science workflow looks like this in general:
- Pose a query.
- Gather information that will assist you in answering that question.
- Clean up the information.
- Data exploration, analysis, and visualization
- Create and test a machine learning model.
- Communicate your findings.
Advanced mathematics, deep learning mastery, and many of the other skills listed above are not required for this workflow. However, it does necessitate familiarity with a programming language as well as the ability to work with data in that language. And, while mathematical fluency is required to excel at data science, only a basic understanding of mathematics is required to get started.
The other specialized skills listed above may help you solve data science problems in the future. To start a career in data science, you don’t need to master all of those skills.
What is a data scientist?
Large sets of structured and unstructured data are collected and analyzed by data scientists. Data scientists combine computer science, statistics, and mathematics in their work. They perceive data analysis as its processing, and modeling results to develop constructive and positive for businesses and other organizations.
Data scientists are analytic experts who use their knowledge of technology and social science to identify patterns and manage data. They uncover solutions to business challenges by combining industry knowledge, contextual understanding, and skepticism of existing assumptions.
A data scientist’s job is to decipher unorganized data from sources such as connected devices, social media feeds, and email messages that don’t fit neatly into a dataset.
Now, let’s get to the topic on hand, how to pursue a career in Data Science, here’s how!
Pursue an undergraduate degree in data science or a closely related field
To work as an entry-level data scientist, you’ll need at least a bachelor’s degree in data science or a computer-related field, though most data science jobs will require a master’s degree.
Degrees also give a structure to your résumé, internships, networking opportunities, and recognized academic credentials. If you have a bachelor’s degree in another field, you may need to focus on developing job-specific skills through online short courses or boot camps. Check out this interactive course on Data Analysis to get you familiar with data fundamentals.
Learn required skills to become a data scientist
- Machine Learning techniques
- Data Visualization and Reporting
- Risk Analysis
- Statistical analysis and Math
- Effective Communication
- Software Engineering Skills
- Data Mining, Cleaning, and Munging
- Big Data Platforms
- Cloud Tools
- Data warehousing and structures
Consider a specialization
Data scientists can specialize in a specific industry or develop strong skills in areas like artificial intelligence, machine learning, research, and database management. Specialization is a good way to boost your earning potential while also doing work that you enjoy.
Get comfortable with Python
Python and R are both excellent data science programming languages. Although R is more popular in academia and Python in industry, both languages have a large number of packages that support the data science workflow.
To get started, you don’t need to know both Python and R. Instead, concentrate on mastering a single language and its ecosystem of data science software. If you’ve decided on Python (which I recommend), you might want to consider installing the Anaconda distribution, which makes package installation and management easier on Windows, OSX, and Linux.
You also don’t need to be an expert in Python to proceed to the next step. Rather, concentrate on learning data types, data structures, imports, functions, conditional statements, comparisons, loops, and comprehensions.
Try practicing with datasets on Kaggle to improve your familiarity with the programming language.
Learn data analysis, manipulation, and visualization
Yes, of course, data analysis is a part of data science. It was quite direct, well let us go with the flow. Students and learners are often confused about these two. Basically, data science is a broader term than data analysis.
Let us read how? Right Now,
Difference between data science and data analysis
Data analysis is more effective when it is focused, with specific questions in mind that must be answered using existing data. Big data analytics focuses on finding answers to questions, whereas data science produces broader insights that focus on which questions should be asked.
Data Analytics is designed to uncover the specifics of extracted insights, whereas Data Science focuses on finding meaningful correlations between large datasets. To put it another way, Data Analytics is a subset of Data Science that focuses on more detailed answers to the questions that Data Science raises.
To cut a short story long, Data science refers to a group of disciplines that are used to analyze large datasets. Data analytics software is a more focused version of this, and it can even be considered part of the overall procedure. The goal of analytics is to produce actionable insights that can be used right away based on existing queries.
So, now that you know the difference then it is needless to say that data analysis is very important to learn.
Business strategies and marketing tactics are greatly influenced by data specialists. Companies, large and small, now expect data-driven insight to inform their business decisions. Business strategies and marketing tactics are greatly influenced by data specialists.
Data visualization aids in the telling of stories by transforming data into a more understandable format and highlighting trends and outliers. A good visualization tells a story by removing noise from data and emphasizing important details.
Data manipulation is important because it allows you to quickly access information that is crucial to your business and objectives. As your company grows or changes due to market demand, this process can be tailored to identify different data sets.
Understand machine learning in more depth
Simply put, we contribute to Machine Learning every day through our internet interactions. You see Machine Learning in action all the time, whether you’re searching for a coffee maker on Amazon, “top tips to lose weight” on Google, or “friends” on Facebook.
Process automation will soon replace the majority of human labor in manufacturing. Devices must be intelligent to match human capabilities, and Machine Learning is at the heart of AI.
Data Scientists must fully comprehend Machine Learning in order to make accurate prognostications and estimates. This could help machines make better decisions and take smarter behavior in real-time without requiring human interference.
Data mining and interpretation are being transformed by machine learning. Traditional statistical techniques have been replaced by more accurate automatic sets of generic methods.
Review additional data scientist certifications and post-graduate learning
Here are a few accreditations that concentrate on practical skills:
Analytics Professional Certification (CAP)
The Institute for Industrial Engineering and the Management Sciences (INFORMS) developed CAP, which is aimed at data scientists. Candidates must demonstrate their understanding of the end-to-end analytics process during the certification exam. This includes business and analytics problem framing, data and methodology, model development, deployment, and life cycle management.
SAS Enterprise Miner 14 SAS Certified Predictive Modeler
SAS Enterprise Miner users who perform predictive analytics will benefit from this certification. Candidates must have a complete understanding of SAS Enterprise Miner 14’s predictive modeling features.
Start your career as a data scientist at the entry level.
You should be ready for your first data science role once you’ve acquired the necessary skills and/or specialization. Creating an online portfolio to showcase a few projects and your accomplishments to potential employers may be beneficial.
Because your first machine learning job may not be a data scientist position, but rather an explanatory role, you should select a company with the potential for expansion. You’ll quickly pick up teamwork skills and best practices that will help you advance to more senior positions.
Obtain a master’s in data science.
Academic credentials might be more important than you think. Is a master’s degree required for most data science jobs? It varies by job, and some working data scientists have a bachelor’s degree or have completed a data science Bootcamp.
According to 2019 data from form trustworthy sources, over 90% of data scientists have a graduate degree.
Well, this is how one can make a successful career in data science with good remuneration. Yes, they are well-paid professionals. They earn a very good salary but it is important to know what actually are the responsibilities of a data scientist for which they are compensated so well.
Responsibilities of data scientist
- Undirected research and framing open-ended industry questions to solve business problems.
- Use effective data visualizations and reports to communicate predictions and findings to management and IT departments.
- Make cost-effective modifications to current procedures and strategies.
- To prepare data for predictive and prescriptive modeling, use sophisticated analytical methods, machine learning, and statistical methods.
- To prepare the data for preprocessing and modeling, thoroughly clean it to eliminate any irrelevant information.
- Making programs to optimize repetitive tasks and coming up with new algorithms are used to solve issues.
Well, now it is time to go for the most important characteristics of a good data science professional or data scientist.
Characteristics that must be there in Data scientist
Data scientists should be naturally curious about their surroundings, but through an analytical lens, rather than just knowing programming languages, database management, and how to transpose data into visualizations.
Data scientists may have personality traits that resemble quality assurance departments as they review large amounts of data and look for patterns and answers. They’re also inventive when it comes to developing new algorithms for crawling data or designing well-organized database warehouses.
Professionals in the data science field must be able to communicate in a variety of ways, including with their team, stakeholders, and clients. There may be a lot of dead ends, wrong turns, or bumpy roads along the way, but data scientists must have the drive and grit to stay afloat in their research with patience.
A data scientist’s average annual salary is Rs. 698,412. An entry-level data scientist can earn around 500,000 per year with less than a year of experience. With 1 to 4 years of experience, data scientists can expect to earn around 610,811 per year.
Earning a bachelor’s degree in data science or a related field is typically the first step toward becoming a data scientist, but there are other options for learning data science skills, such as attending a Bootcamp or joining the military.
Before landing your first entry-level data scientist job, you should consider pursuing a specialization or certification in data science, as well as earning a master’s degree in the field.
For a highly interactive and well-designed specialization course in data analysis, visit here.