The Experfy insights provide cutting-edge perspectives on Big Data and Analytics. Experfy’s unique ability to focus on business problems enables them to provide insights that are highly relevant to each industry. With its certification for Big Data Analysis and Big Data Science, Experfy can make you a master in these fields.
A considerable confusion prevails with job seekers regarding data science, big data and data analytics for preferring a career role. Experts in data engineering, data science, data analytics, data mining and many related fields in data science often work side by side with respective individual functions, but mistakenly many people interchange functional roles in these fields.
Let us look at the Data Science roles of Big Data Developer, and Big Data Analyst to figure out who they are and what they are used for, and the skills you need to expertise in either of these data fields.
Data Science deals with both unstructured and structured data, and everything needed for the arrangement, analysis, and cleansing of the data. This field combines statistics, mathematics, data capturing, programming, data cleansing, problem-solving, producing and adjusting the data and studying the phenomena from various views. In plain terms, this field is a combination of many techniques applied in picking up data information and their insights.
Gartner defines Big Data as “High-volume, and high-velocity and/ or high-variety information assets that need forms of information processing that are cost-effective and innovative and can allow enhanced decision making, insight and process automation.” This massive data volume becomes difficult for processing by the conventional techniques. Nonaggregated data becomes the point of starting Big Data processing, the storing of which is difficult in just one computer’s memory. Every day, Big data floods businesses with huge amounts of structured and unstructured data. Some process is needed to analyze insights from such Big Data to promote good decision making to make business fruitfully move forward as strategically planned.
Data Analytics deals with the mechanical processes or algorithmic applications in developing insights from Big data. Almost all businesses use Data Analytics to improve decision making and verify or disprove prevailing models and approaches. Data Analytics centers on inference, and concentrates principally on the interpretation that mainly depends on the analyst’s knowledge.
Sources of Big Data
Big data, the huge data sets generated on the internet are gathered commercially or through free services. This data can be from any source such as a post on social media, sensors, online videos, digital images, online purchase transaction records, mobile phones, traffic on different websites, email messages etc. The essential characteristic of such massive data is that once the data is added it remains over the cloud forever and also remains unremovable. And thus, big data can include electronically stored information of online and offline data and information from personal devices, and even information considered removed.
Another significant contributor of big data is the evolution of sensory systems online, such as some dedicated apparatus and some others like tablet computers and smart phones. And then various connected devices to the IoT that yield enormous business and personal applications. These applications can be home automation sensors connected to the internet, health monitoring, driving or driver assistance, child-care and seniors-care and so on.
The big data evolution into recognizable information enabled the making of open-access systems for forecast services development. Analysis of the huge information available on the web as big data makes risk assessing possible to improve competitiveness. This leads to demand-driven or an application-driven forecasting, a field that receives many benefits from massive data analytics giving best insights to good decisions.
Data mining and utilizing big data
Data mining processes inter-data relations by analyzing connected dots between them to find concealed values and relations that can give new insights. This process makes much-needed information available to any business to explore ways to decrease their costs and increase revenue. This becomes a game changer with big data cloud repository turning into an actionable technology for those who can harness it to their advantage. Data mining analyzes relationship between inter-data with the connected dots and find meanings that can provide new insights.
What is your role as a Big Data Developer?
Your assets as a Big Data Developer enables companies in delivering high-quality IT Service Management. As a Big Data Developer, your role is to see how Big Data can be dealt with as a superior substitute of the conventional techniques in the suggested market. This follows the core factors of Big Data, awareness of various distributed computing program models and their management. You also have to figure out how the NoSQL database works exceptionally and effectively when correlated with alternatives. You have to possess insight on growing, debugging, optimizing and setting up the different Big Data programs like Pig, spark, Map Reduce, and Hive, and interpreting the theories referred to the Big Data Administration. Big Data Developer can also be known as IT Engineers, Data Warehouse Developers or Software Developers and build their career in Big Data and Hadoop and more.
A big data developer knows the technologies such as Apache Spark or Hadoop and knows how to process parallel data. From the perspective of programming, focus is on Scala, Python and Java. He knows functional programming applications. He possesses a deep knowledge of the big data platforms ecosystem and the tools that can transfer data to a big data platform or address stream processing.
A Big Data Developer closely functions with a big data systems engineer that knows Hadoop and other similar technologies, hardware requirements, and resource management. He also works with Big Data Analysts that have a good understanding of statistics and mathematics and machine learning algorithms for applying to the data provided by a big data developer.
What are the requisites for a Big Data Developer?
As a Big Data Developer, you need to possess a proven comprehension of big data applications. A good knowledge of SQL, Node.js, core Java, JS, OOAD, and other similar scripting languages. And more related tools that are used in big data development such as R and Python. A database foundation structures, principles, theories, and analytical, problem-solving, MapReduce and code writing skills.
Source: Data Science Dojo
The main skill you need to have is to know how the machine is performing the tasks given to it and its implications. The other key skills for developing big data systems include the knowledge of the transaction size, the paths to follow from the data or a command, the time to travel the bandwidth, latency and data size. This must be linked to seeing how different languages or systems work at lower stages and the lines that various algorithms require taking. It all includes language/library/system implementation skill, I/O for storage and networking at every level.
What is your role as a Big Data Analyst?
Big Data, a collection of huge amounts of data, needs appropriate database management systems for its analysis to derive useful insights. Analysis and insights from this data are considered as Big Data Analytics. This aims to solve queries and become a business-friendly tool for decisions. Also, the usage of queries and various processes related to Data Aggregation are a part of Data Analytics.
Businesses use big data to better understand and target their respective customers. Using big data enables a retailer to anticipate what products sell, a telecom carrier to predict when a customer might switch carriers, and the likes. There are many fields where big data analytics help improve businesses and human lives alike.
Big data analytics enables finding new cures and predict the spread of a disease, help police catch criminals and even predict criminal activity, enable credit card companies to detect fraudulent transactions. A number of smart cities use big data analytics where a bus knows to wait for a delayed train and where traffic signals predict traffic volumes and operate to minimize jams. Big data is important to everyone due to its application in almost every field, and affecting everyone’s life in one way or the other.
Can a Big Data Analyst become a Big Data Developer?
It is not difficult for a Big data Analyst to become a Big data Developer. Analytics deals with maths and statistics. Development deals with the software skills along with the maths. You as an Analyst have the mathematics skills you need to learn to program and know the technologies used in analytics.
The difference between a Big Data Developer and a Data Analyst
Big Data Developer also known as Data Scientist, Data Engineer or Software Engineer generally operates under the Engineering wing. They set up the data programs that provide data to a data platform. They run programs like Hadoop, Spark, Custom Code, ETL tools, etc. to develop data pipelines for building and managing the data program. And to succeed in this task you need to be a strong technical chopper.
Data Analysts generally operate under any business stream, such as operations, strategy, product, growth, sales, marketing, etc. Data Analysts link the data platform and the business stream. They use the shared data platform and help solve business issues managing data from the data platform. They need to manage a great harmony between business and technical skills to be successful in this role.
The Data Analyst role is not an easy task
Data Analysts also take a significant part in Data Science. They play a range of tasks associated with setting up data and collecting statistical reports out of them. They further display the data in the design of tables, graphs, and charts, and apply the same to make related databases for businesses. A Data Analyst can be known as a Data Architect, a Database Administrator or an Analytics Engineer.
What are the requisites for a Big Data Analyst?
A Data Analyst conducts full life cycle activities that include requirements analysis and design. The Data Analyst must develop business intelligence and reporting capabilities, and he continuously monitors performance and quality control plans to identify improvements. As a Big Data Analyst, you need to be familiar with data warehousing and business intelligence concepts, besides in-depth knowledge of SQL and analytics. You also need to have a strong understanding of Hadoop based analytics like HBase, Hive, Mapreduce, Cascading etc. Besides, familiarity with different ETL tools for transforming different sources of data into analytics data stores; you also require data storing and retrieving skills and tools. You have to be able to make some critical business features in real time and decision making.
Why Big Data Analytics?
Big Data Analytics helps many businesses to improve their performance. As we all know big data consists of both structured and unstructured data from various online and offline sources, devices, sensors, instruments, etc. A Data Developer with his programming tools can gather such data from various sources, but analytics is a must for processing the data. Through processing the Data Developer can derive some insights from such data to know what they mean and how the information inferred can be used for business development.
Data Analyst Training and Certification
Experfy provides Big Data Analytics and Tech Training to advance your career. Developed by industry thought leaders and Harvard Innovation Launch Lab based Experfy, the certification focuses on domain-specific use cases. This way you are productive as soon as you leave the classroom.
The Experfy Data Analyst Training and Certification courses cover the following topics using a wide range of business intelligence and data science tools:
- Interpret data, analyze results using statistical techniques and provide ongoing reports
- Develop and implement data collection systems and other strategies that optimize statistical efficiency and data quality
- Acquire data from primary or secondary data sources and maintain databases/data systems
- Identify, analyze, and interpret trends or patterns in complex data sets
- Filter and “clean” data, review dashboards, and performance indicators to locate and correct code problems
The Experfy Data Analyst track will cover techniques and how to use tools for programming, statistics, machine learning, data mining, data visualizations. Courses are in active development for all big data tools and technologies such as Hadoop, Tableau, Qlik, SAS, SPSS, Cognos, and Statistica, to name some of them.
According to Indeed.com, the average annual salary of a Business Intelligence Analyst in the U.S. is $92,000, a Tableau Consultant is $102,000, QlikView Consultant is $102,000, and a Cognos BI Manager is $135,000.
Data Science Training and Certification
Learn data science from industry experts at Harvard, Columbia, Cisco, Apple, and Google. Experfy instructors are industry thought leaders who provide you with in-depth training in introductory topics like statistics to advanced ones like machine learning. This Data Science Certification Program covers the concepts and tools you will need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. As you work your way through different courses in the data science training track, you will develop a portfolio of projects that you can showcase during interviews. Employers want to see students who have been trained by real experts and not by training departments. Experfy courses give you the practical hands-on training that will prepare you for the real world skills that will be necessary as you begin working as a data scientist.
Demand for Data science talent is exploding. Once you complete the training track on data science, you will know how to build and derive insights from data science and machine learning models. You will learn key concepts in data acquisition, preparation, exploration, and visualization along with working on real world use-cases. Data Science is an essential skill for analyzing and deriving useful insights from data, big and small. McKinsey estimates that by 2018, a 500,000 strong workforce of data scientists will be needed in the US alone. The resulting talent gap must be filled by a new generation of data scientists.
According to Indeed.com, the average annual salary of a Data Scientist in the U.S. is $123,000.