The definition of data analytics captures the broad scope in itself, it is the process of analyzing raw data to find trends and answer questions. The process of data analytics is quite time-consuming and includes many techniques with many different goals. But successful data analytics can solve numerous problems as it will provide a clear picture of where you are, where you have been, and where you should go. The use of data analytics goes beyond maximizing profits and ROI, however. Data analytics can provide critical information for healthcare (health informatics), crime prevention, and environmental protection. These applications of data analytics use these techniques to improve our world.
uCertify introduces the Foundation of Data Analytics course and lab which aims to enhance the latest knowledge and skills of data analyst professionals. The course is well equipped with interactive tools like objective-based lessons, test preps, and live labs for a hands-on experience. It will assist one in enhancing their knowledge of how to use data typologies, data analytics tools, business statistics, data visualization with the working and value of data, and many more. The course offers a complete learning path for you by including all the subject areas on which the exam is based, beginning with processing, collecting, storing, and analyzing to help you in advancing your professional career.
uCertify’s course Foundation of Data Analytics carries all the requisite factors to be considered as a foremost choice of any student and instructor. It has well descriptive lessons, valuable quizzes, flashcards, and glossary terms to get a detailed understanding of the distinctive course. The availability of test prep makes it more reliable for the preparation of certification as it consists of pre-assessment questions and practice questions to keep a check on your preparation route. The video tutorials and performance labs accompany you to get hands-on with all the skills required for it. So don’t wait for long and get your Foundation of Data Analytics course from uCertify today.
Data analysts and data scientists both work with data, the difference is in the work that they do with it. Data analysts inspect sets of data and use it to identify trends, develop charts, and create visual presentations. With the help of the results provided by Data analysts, businesses can make more strategic decisions. On the other hand, Data scientists create and formulate new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.
Operating Data Analytics
The job role of data analysts varies across industries, however, data analysts usually use data to understand and solve problems. Data analysts work on a wide range of fields including business, finance, advertising, operations, pricing, and international strategy analysis. Data analysts evaluate well-defined sets of data using different tools to answer business demands. This knowledge will help find answers to the questions like why sales decreased this quarter, why a marketing campaign didn’t do well in all regions, what internal factors affecting revenue, and more.
Data analysts have skills including data mining, data modeling, R or SAS, SQL, and database management and reporting.
Data analysts usually design and maintain data systems and databases with the help of statistical tools to depict data sets. They are also responsible for preparing reports that reveal movements, patterns, and predictions based on useful results.
Operating Data Science
Data scientists measure the required or undiscovered results by asking questions, creating algorithms, and statistical models. Unlike Data analysts, Data scientists do not deal with heavy coding. Data scientists work with limitless data sets with multiple tools and build their automation systems and frameworks.
A Data scientist should have practical and working knowledge of machine learning, software development, Hadoop, Java, data warehouse, python, object-oriented programming, and more.
Data scientists are mostly responsible for designing data modeling processes, as well as creating algorithms and predictive models. These models and algorithms help in extracting required information and solving complex problems.
Data Science or Data Analytics: which one to choose?
You can access to choose the right path for you after understanding the differences between data analytics and data science and identifying each career’s requirement. There are three key factors you should consider before deciding which career path is the best-suited choice for your professional goals.
1. Choosing the right education background
Though there are many similarities in the work of Data Scientists and Data analysts, you need different educational backgrounds for each.
Data analysts examine large data sets to identify trends that help businesses make important decisions. Pursuing an undergraduate degree in a science, technology, engineering, or math (STEM) major can provide the required skills to perform these tasks. Some professionals prefer to have an advanced degree in analytics or a related field. Professional experience in programming, databases, modeling, and predictive analytics is also suited for this job.
Data scientists are responsible for designing and constructing new processes for data modeling and production. In this process, they use data mining and machine learning. That’s why a master’s in data science is considered as an essential education prerequisite for professional advancement.
If you’re a student and thinking about which stream you should choose then you may find a data analytics role more attractive. Employers prefer candidates with an undergraduate degree for these positions. But if you have decided to devote yourself to an advanced degree then you would like to go for Data scientists’ roles.
2. Choosing your area of interest
If you love working with numbers and statistics from or are interested in computer science and business, then these jobs are right for you.
Data analysts deal with numbers, statistics, and programming so they love these fields. A Data analyst has to work in databases to discover data points from complex sources because they work as the protector of an organization. They should also understand the work culture and nature of the industry they work in.
Data scientists are required to understand math, statistics, and computer science and have knowledge of the business world. If this characterization matches your educational background and experience then a data scientist role is the right pick for you.
In both cases, you should have an understanding of which career matches your interests. This will help you select what kind of work you’ll enjoy.
3. Choosing your desired salary and career path
The pay scale of data analysts and data scientists’ roles varies with the level of experience.
Data analysts’ pay scale varies between USD 83,750 and USD 142,500. They mainly have to deal with the databases, but their salaries can be increased by learning additional programming skills, such as R and Python. Researches have shown that data analysts with more than 10 years of experience earn more money and receive promotions.
Data scientists with a graduate degree are generally more experienced and are considered more superior to data analysts. That’s why they are better paid. According to reports, data scientists receive an annual salary between USD 105,750 and USD 180,250. Data scientists achieve more promotions as well. They can advance to senior roles such as data architect or data engineer.
Data analysts and data scientists differ in responsibilities, educational requirements, and career trajectory. A qualified professional for data-focused careers are highly demanded these days because of businesses increased reliability on data. So, now when you know about all the deciding factors for choosing between data scientists and data analysts, you will be able to decide which career is the right fit for you. uCertify will help you with the next step, gaining the correct skills to become a data scientist or analyst. Our comprehensive courses and other learning resources will teach you everything you need to know. So, start learning with uCertify today!
uCertify has introduced a new CIW Data Analyst course to help candidates prepare for the latest 1D0-622 exam. This course covers 1D0-622 exam objectives and teaches you to use data to analyze a company’s operation and make appropriate business decisions. The course focuses on Web-oriented data, and methods for analyzing data in order to create appropriate dashboards, reports, and solutions. The CIW Data Analyst course also provides skills to identify sources of institutional knowledge that include Customer Relationship Management (CRM) applications, transaction data, social media, marketing sources, inventory management systems, and industrial systems. It offers specific tactics for working with cloud-based data, that include backup procedures, security issues, cloud-native data, migrating data to or from the cloud, and user training.
After completing the uCertify course, students will learn about:
Fundamentals of Data Analysis and Big Data
How to work with Data Sources
Tools for capturing and analyzing data
How to analyze and report data
The Data Analyst certification is a part of the CIW Web and Mobile Design Series. It validates students’ knowledge to compare structured and unstructured data and find how data can drive business decisions. After earning this certification, students will learn to regulate relationships between organizational efforts and business outcomes, analyze and represent data, and extrapolate information using data obtained from new and traditional data sources. Students will also understand how ethics and security are vital parts of a Data Analyst’s responsibilities. This certification helps professionals working in marketing, merchandising, and data-driven fields. It is also helpful for data analysis professionals, product development professionals, merchandising professionals, web marketing professionals, advertising professionals, and entrepreneurs. On earning the certification, candidates will be able to compile the data from many sources, prepare and deliver an objective, and unbiased presentation.
Want to learn about Data Structures and Algorithms in Python? Then uCertify is the right place for you. We offer a comprehensive course on data structures and algorithms in Python along with interactive learning resources and performance-based labs. Labs come with live and virtual environments where students learn by doing. They encourage exploration and experimentation in a risk-free environment resulting in better learning for students. uCertify course covers Python primer, Object-Oriented Programming, algorithm analysis, recursion, array-based sequences, stacks, queues, and deques, linked lists, trees, priority queues, and more. The course provides a Python-centric text for the data structures. The syntax and powerful features of the language have been used throughout and the underlying mechanisms of these features are fully explored.
Data Structures and Algorithms are some of the significant skills that every computer science candidate must-have. It is often seen that candidates with a good knowledge of these technologies are better programmers than others. Selecting the correct data structure or algorithm to solve a particular problem impacts the efficiency of the solution. In recent years, more colleges have started to adopt the Python language for introducing students to programming and problem-solving. Python provides several benefits over other languages such as C++ and Java as it has a simple syntax that is easier to learn.
uCertify courses offer interactive activities that use almost any media element including, but not limited to, text, images, animation, audio, and videos. These activities can be used in both summative and formative assessments that enhance student’s learning outcomes. Our courses work well in all three modalities: traditional classrooms, online, or blended. Instructors can efficiently manage their students with tools such as create and manage sections, create assessments, and so on.
So, what are you waiting for? Start learning data structures and algorithms in Python today with uCertify!
In a world where 2.5 quintillion bytes of data are produced every day, a professional who can work on this huge amount of data is indispensable! Let’s discuss Data Science career opportunities and why they are among the most demanded career options of the 21st century. This article will provide you with that knowledge, so you can spend your time efficiently and choose the right career option for you. The first step is figuring out what career you want actually. Where can your new data science skills take your career? Which path is right for you?
Available Career Options
Data Scientist jobs are the most desired and demanded jobs in the Big Data Analytics and IT industry. Experts predict that Data Science career opportunities will only increase in the coming years! Skilled professionals who can perform operations on Data to provide business solutions are very few. This situation has led to the high demand for skilled professionals who can perform roles like Data Scientists and others.
Various Job Roles
Data Scientists are not only responsible for business analytics, but they are also involved in building data products and software platforms, along with developing visualizations and machine learning algorithms.
Some of the Data Scientist job titles are:
Business Intelligence Manager
Demanded Data Science Skills
To become a successful data scientist you will require coding skills along with knowledge of statistics, and critical thinking. Some of the in-demand Data Scientist skills are:
Databases: SQL and NoSQL
Statistics and Applied Mathematics
Machine Learning and Neural Networks
Creative Thinking & Industry Knowledge
Programming Languages: R/Python/Java
Working Knowledge of Hadoop and Spark
Proficiency in Deep Learning Frameworks: TensorFlow, Keras, Pytorch
uCertify offers various comprehensive Data Science courses that help you gain expertise in Data Science and other fields. You can learn concepts with the help of real-world scenarios in our course.