Career Scope and Benefits of Data Science
Either you pick up the time and place to change or change will pick up the time and place for you! Either way, change is inevitable and that’s the reality. While there is an ever-growing demand for good data scientists in every domain right, those who start their learning journey now will be the ones with the head start in their careers compared to others.
What is Data Science?
Data science is a discipline that utilizes mathematical techniques, procedures, algorithms and programs, to derive knowledge from a range of textual and non-structured data. Data science involves data processing, and big data.
Advancements in technology helped data science evolve from cleaning datasets and applying statistical methods to a field that encompasses data analysis, predictive analytics, data mining, business intelligence, Machine Learning, Deep Learning, and so much more. Now, there still might be some who think that data science is just a trend and the hype around it will eventually go away. Of course, nothing could be farther from reality.
Why Data Science With Python & R?
Already we’re aware how potential is Data science, what if we combine it together with the combination of Python and R programming languages? Python presents a greater-level source code, perceived, and good approach to object-oriented programming. It’s among the best languages for different data sciences and applications used by data scientists. Python offers excellent scientific functionality.
On the other hand, Owing to its simplicity in the field of statistics, R is very significant in data science. R is normally used in data science where special data analyzes for independent or distributed computing are required. R is suitable for discovery, too.
Why is Data Science Important?
Retail, e-commerce, logistics, transport, health care, finance , insurance, property – all of which require a strong data science team capable of leveraging information within their organization to achieve a competitive advantage. Therefore, you can certainly pursue the field in data science if you are searching for a satisfying profession with a significant effect on the company decision-making cycle.
According to a study, the skills projected to grow the fastest include advanced analytical competencies, such as Data Science, Machine learning, Tableau, Big Data, Data Visualization. Of course, skills in SQL, data analysis, financial analysis, data management, mathematics, data warehousing, SQL Server, database administration, business intelligence, and ETL firmly remain among the most demanded analytics skills.
Why are data science career opportunities worth exploring?
The Data science job market never ceases to offer an abundance of rewarding career opportunities. Landing a job in the field guarantees you an average annual income ranging from $63,000 to more than $100,000!
According to the Data Science Jobs Report, by 2020 the number of data science and analytics job listings is estimated to grow by nearly 364,000 listings to about 2,720,000 openings. In fact, there is a serious concern that the data science skill gap will continue widening and the supply of data scientist and data analytics career talent won’t be able to catch up to the industries` demand. Therefore, now is literally the best time to become a data science specialist.
Important aspects of Data Science you should learn
Here’s everything you should be knowing to become a data scientist-
Data Science Fundamentals
Intro to Data and Data Science, Introduction to Microsoft Excel, Advanced Microsoft Excel, Probability, Statistics, Mathematics, Introduction to Tableau, and Power BI.
Programming for Data Science
SQL, SQL+Tableau, Introduction to Python, Python Programmer Bootcamp, Git and GitHub, Introduction to R Programming.
Machine & Deep Learning
Machine Learning in Python, Deep Learning with TensorFlow, Deep Learning with TensorFlow 2.0, SQL+Tableau+Python.
Advanced Specialization
Credit Risk Modeling in Python, Time Series Analysis in Python, Customer Analytics in Python, Web Scraping, and API Fundamentals in Python, Python for Finance, Introduction to Business Analytics.
Data Science Salary & Jobs
To help you sort things out, here’s what you should know about the top 5 in-demand types of data science jobs – data analyst, data engineer, data architect, and, of course, data scientist.
Who is a Data scientist?
A data scientist has an unrivaled range of expertise to their company’s benefit. Not only can data scientists grasp the data vocabulary, they also interpret it and derive useful conclusions from it. They have perfected the art of information storytelling to a stage that these leadership and decision makers comply and start implementing their business model.
As a data scientist in the U.S., even with zero experience, you can get a $10,000 annual bonus on top of your $100,000 average annual pay. And, just in a few years’ time, your earnings can increase to $118,000, plus $12,000 in bonuses!
Who is a Data Analyst?
Real data analysts are the data science soldiers. They are interested in data analysis , data organizing, model formation and organizational growth, and the preparation of specialized analyzes that clarify the trends of the already developed data. The fundamental part of predictive analytics is also ignored by a data analyst.
If you’re taking the first steps in your data analyst career, you can expect an average pay of $57,000. As you reach 4-6 years of experience, your compensation will also go higher ($68,000 median annual salary and an average bonus of $4,705).
Who is a Data engineer?
Data engineers implement complex, large scale big data projects with a focus on collecting, managing, analyzing and visualizing large datasets. All that massive amount of overwhelming raw data? Well, they are the ones turning it into insights using various toolsets, techniques, and cloud-based platforms. Data engineers are responsible for building and maintaining ETL pipelines which make crucial data accessible for the entire company. They also lend a helping hand to BI analysts by designing and supporting BI platforms.
In the U.S., the average pay for a data engineer who’s just getting started in his career is $103,000.
Who is a Data Architect?
You have an imaginative and critical mind? Are you an enthusiastic and interested solver in developing robust database systems? You would then be ideal as a data architect for a job. Data Architects are technological specialists who customize data flux and data management techniques for a broad range of businesses and solutions. They continuously develop the processing and storing of the data. In fact, system architects monitor user exposure (all of which you business agents – you decide who you intend to search now).
According to Glassdoor’s salaries report, the average annual pay for entry-level data architects in the U.S. is approximately $104,000, while professionals with 4-6 years of experience make more than $125,000
Where can you learn Data science?
Demand for skilled data scientists is already shooting through the roof but so is the intense competition for these dream job roles. The sooner you are equipped with the right knowledge and skills, the better it will be for you!
A very important but often overlooked factor of succeeding in the industry is having a good mentor who can guide you through your learning journey and tell you what is relevant and what is not.
A mentor bridges the gap between learning & practical application to business problems. If you wish to learn what is relevant by availing 1:1 mentorship support from industry experts, then you have landed at just the right place! For this Upshot Technologies, the best Data Science Training Institute in Bangalore is a must-try. We present you with the right track to help you excel in your career as a pro data scientist.