The field of Data Science is growing rapidly and has become a lucrative career option. There are now many academic institutions that specialize in the study of Data Science. The learning programs offered by these institutes will help you in building a clear idea of the Data Science industry landscape. Many IT enterprises are working towards bridging their employee analytics skill gap issues with Data Science corporate training programs. The analytics industry is an excellent source of income and there are many jobs available in the industry.
Industries across the verticals like IT, Healthcare, Banking, Insurance, Telecom, Space Research, Education, Finance, Government, and many other sectors offer great opportunities for Data Scientists. The salary packages for the experts in this field range from $143,500 to $ 150,500 and above.
What is Data Science?
The field of Data Science provides a scientific method of collecting and organizing data from various sources in the form of numerical values (numbers) and statistical data (mathematical statistics). Data Scientists analyze the data and communicate the results to business stakeholders. Data Science relies heavily on the tools, algorithms and techniques in Machine Learning and Artificial Intelligence.
Growing Demand for Data Science
Big Data, Data Science, Data Analysis, and Machine Learning are in demand in all areas and industries. Being multidisciplinary in nature, Data Science makes use of techniques from different fields to interpret with Big Data. Data Scientists with knowledge of Machine Learning are very much in-demand. Machine Learning algorithms will be playing a critical role in most of the Data Science processes like Predictive Modeling, Demand Forecasting, etc..
This is a rapidly growing field that offers a new direction in the quest for better information for the betterment of society. Post the COVID pandemic, many companies are relying on data analysis to get their business back on track.
Applications of Data Science
Data Science has become very crucial for enterprises to stay competitive. There are many ways to use this technology. This field helps business stakeholders to get a clear idea about the functioning of their business process. Stakeholders cane make data-driven strategically accurate business decisions. It also helps enterprises with fraud detection and risk analysis.
By analyzing customer data, Data Science can help in building precise marketing campaigns, forecast the demand, and can analyze the market trends. Data Scientists are often used to create reports that project managers can use to communicate their goals with clients and other external parties.
Data Science Project Lifecycle
To successfully complete a Data Science project Data Scientists require a systemic approach. Now let’s have a clear look at the sequence of steps that are involved in the data analytics project lifecycle.
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Data Acquisition
The primary requirement of a data analytics process is data. The primary step involves identifying the source of data that has all the answers to the questions. This data can be collected from a variety of sources namely web servers, social media data, or even from online repositories & even through web scraping. The major problem that arises here is that tracking the source of the data & its accuracy.
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Data Preparation
Data Preparation or Data wrangling is an important step in this process. This is regarded as the most time consuming task & uninteresting task for a Data Scientist. Data collected from different sources is in usable format having missing entries, inconsistencies and semantic errors. Data Scientists adopt manual editing process by editing on a spreadsheet or writing code.
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Hypothesis And Modelling
The is the most crucial aspect in the lifecycle process which includes writing, running and refining the programs to carefully analyze & extract insights from data. Most of the Data Scientists prefer using Python and R programming languages. Machine Learning models are applied for accurate analysis of the data sets.
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Model Design
This process makes use of closeting algorithms like K means or hierarchical clustering. This process determines the likelihood of occurrence of outcomes that we need.
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Building Thing Model
After completing the model design Data Scientists will be working on the process of building the model. Data Scientists will be validating whether the model is apt to achieve the desired business objectives. If you get satisfactory results that you can work towards successfully implementing & deploying the model.
Technical & Non-Technical Skill Sets for Data Scientists
If you are looking for a role as a data scientist, adding data analytical skills to your discipline. You can learn the core technical and non-technical skills in this field with online & classroom based courses and programs that complement you and help you get started. There are many online and offline learning platforms where you can build real-world expertise in Data Science.
Technical Skills:
Being a multidisciplinary field that requires skills in diverse processes, tools, techniques, algorithms and technologies. Budding Data Scientists need to develop skills in multi disciplinary areas to achieve success in their careers. Statistics, Math, Calculus, Linear Algebra, Machine Learning, Predictive Modeling, Deep Learning, AI, Tableau, etc. Data Science Training in Hyderabad course by Analytics Path will help you in mastering core technical and non-technical skills in this field.
Data Scientists should be having strong communication skills and leadership, and decision-making skills. They should be able to effectively communicate their findings from Big Data to the stakeholders and help them make accurate data-driven strategic decisions.
The final step in this process involves communicating the results in the form of rich visuals along with the other team members.
Conclusion:
This article on ezpostings must have given you a clear idea of Data Science. You can build core technical skills and hands-on expertise in this field with Analytics Path advanced Data Science Course in Hyderabad program. This course will present students with skills in Big Data, Data Science, Artificial Intelligence, Machine Learning & Deep Learning course. The trainers in this course are real-world industry experts with 10+ years of domain expertise. Analytics Path Data Science course has a well-structured curriculum. This course lets you work on multiple capstone projects and you can get assistance in resume creation and interview scheduling. This is the perfect perfect to build your career in this trending data analytical field. By the time of this course completion, students can apply for job vacancies across the leading multinational companies. This is perfect opportunity to build a rewarding career in this field.