Big Data Analyst Salary

Big data contains in it patterns and information which when mined can given an insight into customer behavior and preferences. This leads to new innovations, satisfied customers, smoother operations and higher profits. Let’s see the attributes that are making Big Data so popular today:

• Reduced Cost:

With the help of Big Data technologies like Hadoop and cloud-based analysis, organizations find out more efficient ways of doing business and bring cost advantages when it comes to storing huge amounts of data.

• Quick & Improved Decision Making:

With the help of Hadoop and in-memory analytics, organizations can quickly analyze the data and will be able to make decisions based on their learnings.

•  Latest products and services:

Big Data helps businesses gauge customer requirements and preferences, based on which they can develop new products or improve existing products to meet customer needs.

Tons of data are generated every second from our activities on social networks, the internet, or even from traditional business systems. This data generated from various sources is very complex and unstructured, and requires analysis to make it useful.

Data Analytics technologies provide organizations the means to analyse the data and draw conclusions, which further helps them improve their business models and create a better experience for their customers.  Big Data Analytics is an advanced form of analytics which involves several elements like statistical analysis, what-if, and predictive models. There are a lot of tools and applications that enable analysts and data scientists to analyse different forms of data that cannot be handled by the usual BI applications.

The difference between Big Data and Big Data analytics is that the former is a dataset that is not refined and is unstructured and is collected from various sources. Big Data Analytics, on the other hand, is clean and clear data that is easier to access and more concise.

To elaborate, Data Analytics is more accurate and focused than Big Data because instead of gathering dumps of unstructured data, analysts have a specified goal and sort through data to look for solutions to specific problems. Whereas Big Data is just a collection of a huge volume of data that requires a lot of filtering to derive any sort of usage or insight from it.  Additionally, another notable difference between them is that Big Data employs more complex tech tools to handle data, but Analytics uses statistics and predictive modelling with simpler tools.

Big Data Analytics tools include:

1. Tableau Public
2. OpenRefine
3. KNIME
4. RapidMiner
5. Google Fusion Tables
6. NodeXL
7. Wolfram Alpha
8. Google Search Operators
9. Solver
10. Dataiku DSS

There are 7 widely used Big data techniques:

1. Association rule learning
2. Classification tree analysis
3. Genetic algorithms
4. Machine learning
5. Regression analysis
6. Sentiment analysis
7. Social network analysis

There are several techniques that are used for Big Data analysis. Some of them are:

1. A/B Testing: It is a technique in which a control group is compared with a variety of test groups in order to determine what treatments (i.e., changes) will improve a given objective variable, such as say, marketing response rate. This technique is also known as split testing or bucket testing.
2. Association rule learning: This is a set of techniques for discovering interesting relationships, i.e., “association rules,” among variables in large databases. These techniques consist of a variety of algorithms to generate and test possible rules. One application is market basket analysis, in which a retailer can determine which products are frequently bought together and use this information for marketing purposes (a commonly cited example is the discovery that many supermarket shoppers who buy diapers also tend to buy beer).
3. Classification: A set of techniques to identify the categories to which new data points belong, based on a training set containing data points that have already been categorized. One application is the prediction of segment-specific customer behaviour (e.g., buying decisions, churn rate, consumption rate) where there is a clear hypothesis or objective outcome.
4. Cluster analysis: A statistical method for classifying objects that split a diverse group into smaller groups of similar objects, whose characteristics of similarity are not known in advance. An example of cluster analysis is segmenting consumers into self-similar groups for targeted marketing.
5. Crowdsourcing: A technique for collecting data submitted by a large group of people or community (i.e., the “crowd”) through an open call, usually through networked media such as the Web.
6. Data fusion and data integration: A set of techniques that integrate and analyze data from multiple sources in order to develop insights in ways that are more efficient and potentially more accurate than if they were developed by analyzing a single source of data.
7. Data mining: A set of techniques to extract patterns from large datasets by combining methods from statistics and machine learning with database management. These techniques include association rule learning, cluster analysis, classification, and regression.

Big Data comprises of specific attributes in the form of 4 V’s. They are:

1. Volume
2. Variety
3. Velocity
4. Veracity

Big data is present in 3 forms:

• Structured

Structured data is the data that can be processed, stored, and retrieved in a preset form. 

• Unstructured

Unstructured data is the data that lacks any specific structure.

• Semi-structured

Semi-structured data is the data that is the combination of both the formats i.e. structured and semi-structured.

Data is basically the quantities, characters, or symbols on which operations are performed by a computer, which may be stored, transmitted or recorded. Big Data is also data but with a large size. Big Data is a term used to describe a collection of data that is large in size and may grow exponentially with time. Big data sets are so large and complex that none of the traditional data management tools are able to store or process it efficiently. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time.

Data Warehousing is extracting data from one or more homogeneous or heterogeneous data sources, transforming the data and loading that into a data repository to do data analysis which further helps in taking better decisions to improve one’s performance and in reporting.

Big data refers to volume, variety, and velocity of the data. How big the data is, the speed at which it is coming and the variety of data, determine the so-called “Big Data”.  The 3 V’s of big data was articulated by industry analyst Doug Laney in the early 2000s.

Organizations want a big data solution because in many corporations there are many operations associated with a large amount of data. And in those corporations, if data contains much valuable information then it can lead to better decisions, which subsequently leads to more revenue, more profit, and more clients.

Organizations need a data warehouse in order to access reliable data and make informed decisions. Big data solution is a technology whereas data warehousing is an architecture.

Top big data technologies are divided into 4 fields which are classified as follows:

• Data Storage
• Data Mining
• Data Analysis
• Data Visualization

Data storage: For big data projects, cloud-based storage tools are vital to maximizing the amount of information you can store. Cloud storage options let you store data in a secure and accessible fashion, for ease of use.

Data mining: Once you have your data stored, you will need some tools to find the information you want to analyze or visualize. This tool helps you to extract the data you need without the hassle of manually trawling through it all (a task that's impossible for humans to do anyway if you hold thousands or more records).

Data analysis: Got the data you need? Now it's time to find the most powerful tools to help you analyze it in order to glean key insights into your business, your customers or the wider world.

Data Visualization: Not everyone is adept at taking key insights from a list of data points or understanding what they mean. The best way to present your data is by turning it into visuals that everyone can comprehend easily.

Big data analytics is the complex process of examining large and varied data sets, or big data, to uncover information -- such as hidden patterns, unknown correlations, market trends and customer preferences -- that can help organizations make informed business decisions. On a broad scale, data analytics technologies and techniques provide a means to analyze data sets and draw conclusions about them, which helps organizations make informed business decisions. Business intelligence (BI) queries answer basic questions about business operations and performance.

Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by high-performance analytics systems.

Driven by specialized analytics systems and software, as well as high-powered computing systems, big data analytics offers various business benefits, including:

• New revenue opportunities
• More effective marketing
• Better customer service
• Improved operational efficiency
• Competitive advantages over rivals

The Big Data Analytics Certification course from KnowledgeHut has the following features:

• 30 hours of live, intense training on Big Data and the frameworks
• Curriculum that is primarily focused on HIVE, PIG, etc. frameworks
• Case studies and hands-on exercises for learning practical applications
• Learning about Hadoop Ecosystems and different integrations
• In-depth questionnaires on each topic to grasp the concept

Through this course, you will be able to learn the following:

• Basics of Apache Hadoop, data ETL, processing with Hadoop tools, and ingestion.
• Understanding how to join different data sets and use the Pig framework for analysing disparate data.
• Using the Hive framework for organizing data into tables, performing transformations, and simplifying complex queries.
• Using Impala with SQL for performing real-time interactive analysis on large data stored in HDFS.
• Picking the best tool in Hadoop for managing repetitive workflows and achieving interoperability.
• Working on projects like Recommendation Engine, Sentimental Analytics, and Clickstream Analytics.

All data architects, data analysts, data scientists, developers, BI analysts, BI developers, project managers, and SAS developers should take this course.

Here are a few tips that will help you in selecting the best data analytics course:

1. Be clear of the area you want to specialize in - There are many roles in the Big Data Analytics field that are equally in-demand and important. Here are the main 4 roles:
   • Data steward
   • Data engineer
   • Data analyst
   • Data scientist
2. Have an assessment of your current skill level - If you are self-aware of your skillset, you will be able to focus on the areas where you need to work more. It will also help in prioritizing courses that you must take on first. Depending on your current skill level, you can either start with courses that provide a conceptual perspective on application and integration of analytics or you can directly go for deep technical training.
3. Plan your budget- From local to online, there are tons of courses available. But you need to choose the one that suits your needs the best. And even though money should be last on your list of criteria, you should always keep in mind the cost of the training course.
4. Consider the time you can devote- If you want to take the course seriously, you need to consider the time you can actually devote to the course. Make sure that you don't overestimate or underestimate the time you can commit.

The basics that will be covered in the Big Data Analytics course are:

• Programming - It includes Java, Python, C++, R, Ruby, Hive, SQL, SAS, MATLAB, SPSS, Julia, Scala, Weka, etc. Language is not a barrier for a big data scientist.
• Data Warehousing - It includes relational and non-relational database systems like MySQL, DB2, Oracle, NoSQL: Hbase, MongoDB, Cassandra, Teradata, HDFS, CouchDB, etc.
• Computational Frameworks - You must be familiar with the frameworks like Apache Spark, Storm, Slik, Samza, Hadoop, and MapReduce.
• Business knowledge - You need to have the knowledge of the domain you are working on.

With a Big Data Analytics certification, you will be able to make a major career shift. They are proof of competency and are accepted worldwide. As technology keeps changing every few months, getting a full-fledged degree is challenging. Certifications are an easy way to build the required talent. Here are a few benefits of enrolling in the Big Data Analytics certification course:

• The technology is predicted to have great prospects in the future. As Big data is moving from the experimental phase to the real projects, there is a demand for Big Data Analytics professionals.
• With the attention currently given to Big Data Analytics, this has created a huge demand for talent to meet the ongoing demands. The gap in demand and supply has given a huge financial edge to the data professionals.
• When you get the Big data certification, you are starting your journey from a traditional IT job to a futuristic Data Analytics professional. Also, you can specialize in the number of subfields under Big Data Analytics accordingly.
• Getting the Big Data Analytics certification is only a small learning investment. You don’t have to take a break from your career and go to college. There are a number of courses available that you can choose from.
• Today, every sector is trying to leverage the power of big data and analytics. With the Big Data Analytics certification, you will have multiple job opportunities across different industries.

No, there is a huge difference between data science and Big Data Analytics.

• Concept
  • Data Science - Data Science encompasses everything related to data- from preparing to cleaning to analyzing the data, structured and unstructured. It combines mathematics, statistics and problem-solving.
  • Big Data Analytics - It involves applying algorithmic or mechanical processes for deriving insights for complex business solutions. It combines inspection, transformation, cleansing and modelling data.

• Professionals
  • Data Science - They perform exploratory analysis for obtaining insights from data. Different machine learning algorithms are used for identifying the occurrence of a particular event in the future. It focuses on identifying hidden patterns, unknown relations, and market trends.
  • Big Data analytics - They deal with large volumes of heterogeneous data that is captured from multiple sources and is arriving at a high velocity. As a Big Data Analytics professional, you will describe the structure and behaviour of big data solutions and deliver them using big data technologies like Hadoop and Spark, depending on the requirements.

• Required skill sets
  • Data Science - Excellent analytical skills, data management skills, mathematics, statistics, and programming skills. This includes understanding of C/C++, R, Java, Pearl, SAS, python, etc. You can use any of these for Data Science, depending on what you are comfortable with. Also, you must have technical skills associated with data mining, machine learning tools, unstructured data techniques, and data managing skills. Knowledge of database systems and essential business skills are a must.
  • Big Data Analytics - Understanding of R, Python, statistics and mathematics skills, data wrangling skills, data visualization, communication skills, and machine learning skills.

To learn Big Data Analytics from scratch, you should enrol yourself in a training institute like KnowledgeHut. KnowledgeHut provides 30 hours of live training on Big data and its frameworks. The whole curriculum has been created to focus on frameworks like HIVE and PIG. We use hands-on exercises and case studies for helping the participants learn practical applications of Big data and have a clear understanding of Hadoop ecosystem and integrations.

Here is what you can do with your data analytics certificate:

• Increase the chances of landing highly coveted roles like Data Analyst, Business Intelligence Analyst, Data Visualizer, Data and Analytics Manager.
• Be eligible to work across multiple domains like E-commerce, finance, healthcare, retail, entertainment and so on.
• Potential salary increase
• Stay up to date with the latest industry trends

Big Data is used to discover hidden patterns, market trends and customer preferences. These in turn help organizations make informed business decisions, innovate products to meet customer preferences, cut costs, enhance efficiency and reduce time to market.

Data is the new oil and Big data is all about helping organizations beat competition and ensure market survival. In this volatile market where costs are hitting the roof and customers are volatile, it is important that businesses understand the pulse of the market and come up with innovations that meet the customers needs while also being cost, process and time efficient. The analyses of Big Data helps do all of that, and also helps in cost reduction and better decision making, which is why it has become indispensable to organizations.

Big Data analytics is indeed a revolution in the field of IT. It helps organizations to harness their data and use it to identify new opportunities and gain an insight to run their business efficiently. Companies can improve their strategies by keeping in mind customer preference. For example, Netflix Uses Big Data to Improve their Customer Experience.

Professionals who are experts in the field of big data analytics are in huge demand. High profile companies from all sectors hire Big Data Analysts to mine data for useful insights. Below are the list of Companies hiring Big Data Analysts

• Amazon
• EY
• Accenture
• Oracle
• Wipro
• Walmart Labs
• JP Morgan Chase & Co

In Big Data lies the key to unlocking insights that help understand customer preferences and design business strategies. Big data helps a business to figure out what the customer wants and  create new experiences, services, and products to meet these demands. Using big data has been crucial for many leading companies to beat competition and emerge successful. Today, Big Data has changed the way that businesses market themselves and their products. Big data has helped companies innovate, reduce operational costs, increase profits and gain customer loyalty.

Below are the various resources to learn Big Data Analytics:

Big Data Course

• Big Data Analytics Course

Big Data Tutorials

• Guru99
• I2tutorials
• AWS

Big Data Books

• Analytics India
• The Human Face of Big Data
• Big Data: Principles and best practices of scalable realtime data systems by Nathan Marz and James Warren
• Probabilistic Data Structures and Algorithms for Big Data Applications by Andrii Gakhov

Blogs, articles

• Types of Big Data
• Insights Of Analytics And Big Data
• How Big Data Can Solve Enterprise Problems
• Videos
• What is Big Data
• Learn Big Data & Hadoop Concepts 

Yes, you need to have a good understanding of programming languages to learn Big Data Analytics.

No, Big Data is very easy to learn. Especially if you are familiar with a programming language.

• A high level of mathematical ability
• Knowledge of Programming languages
• Business Knowledge
• Familiarity with frameworks such as Apache Spark, Apache Storm, etc.

If you have not added Big Data Analytics skills to your resume yet, then this is the best time to do so. KnowledgeHut provides Big Data Analytics courses that have been crafted by experts which focus on job-readiness and hands-on skills. Build your concepts from scratch and assist your learning through step-by-step guidance on tools and techniques. 

The Big Data Analytics course completion certificate by KnowledgeHut has lifetime validity.

Big Data Analytics expertise will benefit you in the following ways :

• Helps you gain problem-solving skills
• New opportunities for skilled professionals in a variety of industries like aviation, finance, e-commerce, etc.
• You can select any career path such as project management, security, system architecture, banking etc.
• Increase in salary as the experience grows.

According to IDC, worldwide spending on big data and analytics is growing at a compound annual growth rate (GAGR) of 11.9 percent, and revenues will likely total more than $210 billion by 2020. This growth will only increase in the coming years as more and more data gets generated and the need to analyse it for business benefits increases. This has led to an increased demand for data scientists, analysts, and data management experts. Moreover, according to IDC, big data staffing shortage is expected to expand from analysts and scientists to include architects as well as experts in data management. Hence gaining expertise in this domain is sure to help you reap rich dividends in the future.

By the end of this course, you will learn about Big Data and Big Data Challenges. You will also gain knowledge of the Hadoop Daemon Processes, HDFS, Hadoop Installation Modes, and HDFS, Hadoop Developer Tasks, Hadoop Ecosystems, Data Analytics using Pentaho as an ETL tool.

Enterprises have realised the value that Big data analyses provides to their business, which is why there is an increase in demand for Big Data Analytics professionals. The following states the reasons why big data is in high demand:

• Influences project outcomes: Lack of Big Data may cause failure in project management, which might further lead to losses or pitfalls. Big Data analysis deals with trends, patterns, and various other parameters related to the project being worked on. This way, an unexpected variable can be overcome, hence improving project performance.
• Predictive Analytics: To make predictions for the future, the predictive analysis uses various techniques from data mining, statistics, modelling, machine learning, and AI. As per the patterns found in historical and transactional data, risks can be identified along with the various opportunities for the future.
• User experience: Big Data is in high demand because it helps enhance customer experience. Taking data from call logs, social media channels, customer feedback, etc. allows businesses to improve their products as well as customer experience.

Big Data Analysts could earn a gross pay of $84,955 per year.

Note: All information mentioned is based on data obtained in September 2019.

Today, Big Data Analysts are in great demand. Industries like Professional Services, Manufacturing, IT, Retail and Finance hire individuals who are experts in Big Data technology. Here is a list of some of the companies hiring Big Data professionals:

1. Accenture Analytics
2. Fractal Analytics
3. Mu Sigma analytics
4. Cartesian Consulting
5. Hewlett Packard Enterprise
6. Quest HR
7. IFINTALENT GLOBAL Pvt Ltd
8. Data Analytics Company