Data analysts examine data sets in order to draw conclusions about the information they contain. They do this with the help of specialized systems, software, and techniques.
The information provided by a data analyst enables companies to define the products to be offered to customers according to their needs, the marketing strategy to adopt, or the improvements to be made to the production process.
Basically, data analysts help business make more standard operating procedures as well as better decisions.
Also, one thing that should be clear in your mind from the beginning is the difference between Data Scientists and Data analysts – In simple terms, data scientists build the tools and algorithms that can be used to make sense of data, including big data. For this, they utilize technology, machine learning and mathematical principles.
On the other hand, data analysts apply these models to analyze business data of all kinds to help make smarter business decisions. Even the use of excel by businesses falls under the purview of data analysis. A “big data” analyst would just utilize large amounts of data, that cannot be processed by traditional tools like Excel.
This post is about data analysts. If you want to check out more information about data scientists, here’s How to become a Data Scientist in India.
Salary of a Data Analyst in India
Starting salary is around 20,000-30,000 rupees per month, but prospects for monetary growth are immense because of the rise of big data. Salary also depends a lot on your city and the size of the company.
Job Profile (A Day in the life of Data Analyst)
Work-life balance is fine and largely depends on the company that you work for. Expect 8-10 hours of work per day. Most companies allow data analysts to work remotely, but some are still stuck in their old ways. No need to work on weekends.
Your responsibility would be to do reporting and analysis of company data. As you get some experience behind your name, your role would become managerial. Then you would have to manage a team of data analysts and coordinate with the company’s leadership to help them make better business decisions. Growth opportunities used to be great but are now slowing down.
Here are some key technologies that enable Big Data for businesses. These are the skills and tools that you would need to be familiar with –
- Predictive Analytics
- NoSQL Databases
- Knowledge Discovery Tools
- Stream Analytics
- In-memory Data Fabric
- Distributed Storage
- Data Visualisation
- Data Integration
- Data Preprocessing
- Data Quality
If you want to know more about all these components, check out the glossary at the bottom of the page.
In conclusion, Big Data is already being used to improve operational efficiency, and the ability to make informed decisions based on the very latest up-to-the-moment information is rapidly becoming the mainstream norm.
There’s no doubt that Big Data will continue to play an important role in many different industries around the world. It can definitely do wonders for a business organization. In order to reap more benefits, it’s important to train yourself about Big Data management. With proper management of it, you will make sure that your company is more productive and efficient.
A specialized degree is not really required, but it may help. The more important thing here is data management skills. However, most companies demand that you must be a graduate. Also, bonus points if you are from statistics/maths/economics/computer-science field.
One thing to love about being a Data Analyst
“In this field, every day you need to update yourself by learning and experiencing new concepts. Being a self-directed learner, it means that you take initiative to find out what your learning needs are, you formulate learning goals, you identify resources, you choose and implement appropriate learning strategies and evaluate your learning outcomes.”
One thing to hate about being a Data Analyst
“Unrealistic expectations from stakeholders.”
Resources and Tips
- MS Excel should be the first step in your learning journey. This is good reference document that lists all excel functions.
- There will come a time in your learning journey when you would need to make the jump from excel to more advanced stuff like Access and SQL. Don’t be scared when that happens. W3schools is a good place to practice SQL.
- StatQuest by Josh Steamer has tiny packets of information and is quite helpful as a refresher for statistics.
- You can use Kaggle to download datasets and play around with them.
Step-by-step guide to become a Data Analyst
- Start learning on your own as soon as you can. Take a look at the recommended resources as they will point you in the right direction.
- Take science or commerce stream in class 11th. Graduates from the arts stream can also become data analysts, but it’s just that going for science and commerce will reduce the amount of time it will take you to learn the necessary skills.
- Get an undergraduate degree in statistics/math/economics/computer-science field.
- Masters or any sort of further formalized education is not required.
- Internships are your ‘in’ to get into this profession, so make sure that you do tons of internships.
- Do a couple of side projects to build your portfolio and make yourself employment-ready. Companies love this kind of enthusiasm!
- Sit for campus placements. If that doesn’t work out, start applying to “Junior data analyst” jobs on LinkedIn.
1) Predictive Analytics
Predictive analytics hardware and software solutions can be utilized for discovery, evaluation and deployment of predictive scenarios by processing big data. Such data can help companies to be prepared for what is to come and help solve problems by analyzing and understanding them.
2) NoSQL Databases
These databases are utilized for reliable and efficient data management across a scalable number of storage nodes. NoSQL databases store data as relational database tables, JSON docs or key-value pairings.
3) Knowledge Discovery Tools
These are tools that allow businesses to mine big data (structured and unstructured) which is stored on multiple sources. These sources can be different file systems, APIs, DBMS or similar platforms. With search and knowledge discovery tools, businesses can isolate and utilize this information to their benefit.
4) Stream Analytics
Sometimes the data an organization needs to process can be stored on multiple platforms and in multiple formats. Stream analytics software is highly useful for filtering, aggregation, and analysis of such big data. Stream analytics also allows connection to external data sources and their integration into the application flow.
5) In-memory Data Fabric
This technology helps in distribution of large quantities of data across system resources such as Dynamic RAM, Flash Storage or Solid State Storage Drives. This in turn enables low latency access and processing of big data on the connected nodes.
6) Distributed Storage
A way to counter independent node failures and loss or corruption of big data sources, distributed file stores contain replicated data. Sometimes the data is also replicated for low latency quick access on large computer networks. These are generally non-relational databases.
7) Data Virtualization
It enables applications to retrieve data without implementing technical restrictions such as data formats, the physical location of data, etc. Used by Apache, Hadoop and other distributed data stores for real-time or near real-time access to data stored on various platforms. Data virtualization is one of the most used big data technologies.
8) Data Integration
A key operational challenge for most organizations handling big data is to process terabytes (or petabytes) of data in a way that can be useful for customer deliverables. Data integration tools allow businesses to streamline data across a number of big data solutions such as Amazon EMR, Apache Hive, Apache Pig, Apache Spark, Hadoop, MapReduce, MongoDB and Couchbase.
9) Data Preprocessing
These software solutions are used for manipulation of data into a format that is consistent and can be used for further analysis. The data preparation tools accelerate the data sharing process by formatting and cleansing unstructured data sets. A limitation of data preprocessing is that all its tasks cannot be automated and require human oversight, which can be tedious and time-consuming.
10) Data Quality
An important parameter for big data processing is the data quality. The data quality software can conduct cleansing and enrichment of large data sets by utilizing parallel processing. This software is widely used for getting consistent and reliable outputs from big data processing.