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Day: September 16, 2020

What are the Qualifications Required By a Data analyst

Posted on by Imarticus Learning

Data analysts are those who convert mathematical figures and variables into simpler terms that could be understood by the people (usually management) and could thus supplement in a decision-making process. Data analysis as a field has wide applications across industries like marketing, finance, operations, and so on.

Given the widespread demand in the job market, a qualification through a data analyst course is always helpful in creating job opportunities for the people looking to build a career in this domain. However, there is various other qualification that is a pre-requisite to becoming a data analyst.

Completion of Secondary and Higher Secondary (or any other equivalent examination)
Aspirants need to complete their class 10th and 12th board exams with a minimum of 50 per cent aggregate. Students from a science background who have studied mathematics, statistics, economics, computer science, or other such subjects that require high analytical abilities are preferred, however, aspirants from other streams are considered as well.

Bachelor’s Degree

Every aspirant must have a Bachelor’s degree. Various degree holders can get a job as a data analyst. A list is given below

  • B.Math (Bachelor of Mathematics)
  • B.Com (Bachelor of Commerce)
  • B.Stat (Bachelor of Statistics)
  • B.Tech (Bachelor of Technology) Computer Science
  • B.Tech (Bachelor of Technology) Data Science and Engineering
  • B.Tech (Bachelor of Technology) Big Data Analytics
  • B.E (Bachelor of Engineering) Computer Science
  • B.E (Bachelor of Engineering) Data Science and Engineering
  • B.E (Bachelor of Engineering) Big Data Analytics

These Bachelor’s degrees would fetch jobs mostly at an entry-level. Most of the degrees above are in the field of maths, statistics, and so on. A qualified data analyst needs to have that knack of crunching numbers and playing with huge data sets, that is why people who have done their graduation are considered qualified. However, this list is by no means is exhaustive.

Master’s Degree

Post a Bachelor’s degree people can get data analyst jobs. However, to get into roles that are very high-end and require greater professional commitment from data analyst companies usually prefer people who have a Master’s Degree. A list of the most common qualification degrees for a data analyst job is given below

  • M.Math (Masters of Mathematics)
  • M.Com (Masters of Commerce)
  • M.Stat (Masters of Statistics)
  • M.Tech (Masters of Technology) Computer Science
  • M.Tech (Masters of Technology) Data Science and Engineering
  • M.Tech (Masters of Technology) Big Data Analytics
  • M.E (Masters of Engineering) Computer Science
  • M.E (Masters of Engineering) Data Science and Engineering
  • M.E (Masters of Engineering) Big Data Analytics

This list by no means is exhaustive and there are other Master’s degrees people might choose to pursue to get into data analytics. People usually prefer to do a job after the completion of their Bachelor’s and before Masters, as a work experience before Maters is often considered as an important qualification for the aspirant who is applying for the job role of a data analyst after Masters.

Data Analyst Course

An aspiring data analyst can also choose to do online courses on various internet sites like Udemy, Coursera, etc. Although it is not a mandatory requirement like the above three, it is a qualification that helps aspirants in many ways. People can learn a variety of software from this data analytics courses like

  • R Programming
  • Python
  • Apache Spark
  • OpenRefine
  • QlikView
  • Microsoft Excel
  • SAS

This is a qualification sometimes considered by a company for people who are new to the field. Even seasoned data analysts applying for new jobs do certification courses in these fields as most of them are adept with one computer language only and would their job titles sometimes require them to have a working knowledge of others as well.

Also Read: How to Become a Successful Data Analyst 

What Are the Topics Covered in a Data Science Course

Posted on by Imarticus Learning

Data Science consists of six major topics. These are:

  1. Statistics
  2. Linear Algebra
  3. Machine learning
  4. Programming
  5. Data Visualisation
  6. Data Mining

Through a data science course, one can have a better understanding of these topics. These topics are discussed further in detail through the course of this article.

Statistics:
Statistics is the mathematical branch of business which includes the processes of collecting, classifying, analysing and interpreting the numbers to draw an understanding of them and thus, draw a conclusion.
Statistics is implemented in various ways in the field of data science. These are:

  1. Experimental Design: The answers to various questions are found through means of experimentation including samples size, control groups, and so on.
  2. Frequent Statistics: The user is allowed to define the value of the importance of the result of data.
  3. Modelling: Having statistical knowledge is important for the further success of a data scientist, even though it does not see daily use in their lives. Old statistical models are being slowly replaced with the new models.
  4. Linear Algebra: Linear algebra is a section of mathematics which involves the process of linear mapping between vector spaces. It sees use in data science in the following ways:
    1. Machine learning: When working with data that is dimensionally high and involves matrices, linear algebra comes in very handy. It’s component analysis, and regression techniques see the most use along with eigenvalues principals.
    2. Modelling
    3. Optimisation
    Programming

Coding is a very important part of data science and being able to code well is extremely important for any data scientist. Having a background in computer science is thus a large advantage, however, if one does not have such a background then these skills can easily be picked up through a data science course.

Automating tasks not only saves time and effort but also helps make the process of debugging, understanding and maintaining codes simpler. The practical skills involved in programming are as follows:

  1. Being comfortable with data development. Usually, people with a software development background find it easier to work on commercial projects at a higher scale.
  2. Having experience in the database area, such as knowledge of modern databases like NoSQL and cloud as well as on older databases like SQL, is important to any employer.
  3. Teamwork and collaboration are important as most work as a data scientist would be tone in groups. Thus communication with teammates and holding strong relationships would help keep productivity at a maximum.

Important practices here involve:

  1. Maintenance
  2. Avoiding the use of hard values
  3. Documentation and commenting continuously
  4. Refactor the code

Machine Learning

Machine learning is important in data science and has shown use in a large number of groundbreaking technologies like self-driving cars, drones, image classification, speech recognition, so on and so forth. This field is expanding every minute and expanding very quickly. Thus the knowledge of machine learning and its implication would be necessary for any good data scientist.

Data Mining

The process involving the exploration of data and extraction of vital information is called Data Mining. A data science course makes the understanding of such a topic much clearer. The commonly used slang in data mining are listed below.

  1. Data wrangling/Data munging
  2. Data Cleaning
  3. Data scraping

Data Visualization

Even though the term may seem self-explanatory, there is more to it than what we see. Data visualisation is the process of communication of data and its results through pictorial or graphical representation. The goal of it is to communicate the findings of the data in the simplest way for understanding.

Thus a data science course would further equip aspiring data scientists with all the tools in the toolkit necessary for optimal success in their career.

Coronavirus Fears Accelerate Wealth-tech Innovation! How?

Posted on by Imarticus Learning

Introduction

Wealth-tech is one of the trending sectors in recent days. It is wealth management through digital solutions. Wealth management is being done with the help of technology and it gives better results and accuracy. It also helps in faster analysis & research.

Market forecasting can also be done more accurately with the help of wealth tech services. The recent coronavirus outbreak has slowed down the growth of many industries but it will not slow down the growth of wealth tech services. Let us see how wealth tech services are predicted to grow despite the recent Covid-19 outbreak.

How will the Wealth-tech Sector Grow?

The reasons which will help in the growth of wealth tech innovation despite the coronavirus outbreak are:

  • Wealth-tech has influenced automation in wealth management services. New-age technologies like artificial intelligence, data analysis, machine learning have helped in the growth of Wealth-tech services. People can now automate the wealth management services and they don’t even require a professional to physically manage the processes.
    Wealth Management coursesIt can all be done by smart & intelligent applications. The digital platform can be easily managed by wealth managers from a remote location too. All they need is a system and the client’s requirements. This also helps in adopting more efficiently with the new work from home culture.
  • This pandemic has forced companies/firms/HNI clients to opt for digital wealth management services. If it wasn’t for the pandemic, they still would have been taking traditional wealth management services. It won’t be a hyperbole to say that this pandemic is a blessing in disguise for the wealth tech sector. The employees are also shifting rapidly towards digital platforms.
  • Wealth-tech extends its services to smaller companies as well. The digital tools & services can be used by small scale companies that would have been neglected otherwise.
  • Wealth-tech also helps in cost optimization. Smart applications provide data-driven solutions to wealth management problems and reduce the cost of hiring wealth management professionals for the same. Companies can just use smart digital solutions and can easily manage their wealth.
  • HNI or Ultra HNI clients are also happy about the wealth tech services as they can now talk to their wealth manager from a remote location too. The social distancing norms have been introduced due to this ongoing pandemic. Wealth-tech helps in taking advantage of its services while following the social distancing norms.
  • Market volatility & uncertainty is expected to grow because of the ongoing pandemic. It such times, you don’t want wrong forecasting information so that you end up investing in a loss generating venture. The digital wealth tech solutions are proving to be more accurate than traditional methods. The data analysis & forecasting is more robust & on point via the digital mediums. This will help clients in opting for wealth tech services to know about the trends.
    Data Analysis
  • It is expected that AUM (Assets Under Management) sector will grow from $74.3 trillion in 2018 to $145.4 trillion in 2025 with an impressive CAGR (Compound Annual Growth Rate) of 8.8%. The asset management under wealth management is also shifted to online channels and clients are loving this innovation as it helps them to get more accurate details in less cost. It reduces paperwork and the amount of hard work an individual has to put in to go through all the assets & financial resources of any particular individual.

Conclusion

If you are thinking of building a Wealth Management Career then perhaps this is the right time to step in with all the innovation going around.

You can learn more via the Wealth Management Courses available on the internet. This was all about how the coronavirus outbreak will accelerate the growth of the wealth tech sector.