It is a question that a lot of people have asked me umpteenth times! My answer to most of them was that Analytics is all around you-you just need to take the ability to apply Analytics to the business world. Now, this may seem like a declaration of motherhood, made with the intention of not having any clear instructions on how to accomplish it. Yet, the ability to make a career transition to Analytics is more than ever beckons now.
Nearly every major research and data consulting firm on the planet has understood Analytics’ far-reaching implications. Further, they have started to create teams to prepare for the opening of corporate floodgates to embed Analytics in their daily business decision-making processes and shape their strategic thinking. In Analytics, a massive shortage of qualified people can help corporate houses make the most of the data that is being processed and produced at a frenetic speed.
It can be daunting to know data science when you’re just starting your journey, especially so. What learning tool-R or Python? What strategies to focus on? Too much to know from the statistics? Want I to know to code? These are a few of the many questions that you need to answer as part of your journey.
That’s why I thought I ‘d be creating this guide to help people start in analytics or data science. The aim was to create a natural, not very long guide that will set your path to data science learning. This guide will set a structure during this challenging and daunting time to learn data science.
SAS, SPSS, R, SQL, and … Start with whichever tool you can access. Often you’ll be shocked to find that your company does have a device that you figured it didn’t exist. While I was busy negotiating licenses for my team with SAS in one of my previous jobs, a mine colleague, an Actuary, told me that he had seen a SAS session in one of his team leader’s PCs often back. I followed that team member up, and we found we already had a SAS server in place waiting to be used!
Education is not about understanding anything, but about extensively educating significant pieces, and acquiring a sound knowledge of what you are learning. I would rather have a candidate who knows a lot about running a regression in SPSS than a person who has half-baked information. If you can bring together one tool and a few modules/techniques of the method, then you have a better chance to get a job and also get a job.
Pick up and start using a readily available method to you-SAS, SPSS, R (now accessible as an open-source).
As I said before, you must get an end-to-end experience of whatever subject you ‘re pursuing. A challenging problem one faces in getting to grips with is which language/tool you should choose?
It would probably be the Beginners’ most asked question. The most straightforward answer would be choosing any of the mainstream tools/languages available and starting your data science journey. Tools are, after all, merely means of implementation, but it is more important to grasp the definition.
The problem remains: Which choice would be better to start with? On the internet, numerous guides/discussions answer this particular question. The gist is that you start with the simplest language or the one you know the most about. If you are not equally versed with coding, GUI-based tools should be preferred for now. Then, with the coding part, you can get your hands on as you grasp the concepts.
If you mastered the software, your work is just half over. The tricks of the trade must be taught. There are now two choices before you- a) Learn from another seasoned person / s who may be there in your company b) Learn from qualified curricula.
The self-help tutorials won’t provide you with the secret Analytics ingredient that is important to be able to deploy Analytics to solve real-life problems. The outputs from running procs in SAS or SPSS models produce a significant number of statistics. One of the most valuable secrets that only experienced analytics experts would be able to share is knowing which statistics to look at and which ones to disregard.
Now that you’ve selected a job, the next logical thing for you is to make a committed effort to understand that job. It does not only mean going through the role ‘s specifications. There’s a massive demand for data scientists, and thousands of courses and studies are out there to take your hand, you can learn whatever you want. It’s not a hard call to find content to learn from; however, learning it can become if you don’t put work into it.
You can take up a freely available MOOC or join an accreditation program that should take you through all the twists and turns the role that comes with it.
When you are taking a course, consciously go through it. Ignore the coursework, tasks, and all the discussions that take place around the course. For starters, if you want to be a machine-learning engineer, Andrew Ng may take up Machine-learning. Now you must obey all the course materials included in the course with diligence. It also means the assignments that are as critical as going through the videos in the course. Just doing an end to end course will give you a better field picture.
PwC offers this course, and naturally, it’s more weighted towards business practices than theory. However, it does cover the broad range of approaches and resources that companies are using to address data problems today.
This course is provided by Microsoft and is part of their Data Science Professional Program Certificate. Until taking this course, you need to have beginner level knowledge of Python or R, though.
Machine learning is a hot subject right now in data science.
Will you spend much of your time looking for employment? Although putting time into your search is necessary, keeping learning is also a primary responsibility of any data scientist. New technologies are continually evolving. Skills described as “data science skills” are continually changing, so studying can keep you on top of those skills and improve your desire for potential employers.
The idea is essential, but you need to set aside time to work on tasks and get a job. They will allow you to practice what you are going to create in a data science job, boost your portfolio, and build trust when you try to score an interview for a machine learning engineer, developer, or an expert.
In the current organization, analytics. Very often, people find it hard to figure out where to start. The simple thumb rule is to identify data sources and see if data is being gathered in some data repository. When data is obtained in the course of a particular business method or task, the chances are that it must wait for use.
Note always that starting with the low hanging fruits is beneficial. At first, don’t try to construct a predictive model. Your organization won’t be ready for such a sudden significant change; more importantly, you’ll need to earn the organization’s confidence before they start to trust Analytics’ predictive power and ability
Start by extracting essential insights from the data that the business reports do not currently collect. Create simple metrics that will add tremendous value to the companies and interest the essential people in your organization in what you are doing. I once talked to a mining client (who was in direct sales) with the best BI system in place, but they didn’t have well-defined metrics to optimize the BI system. They had not even known such simple facts as:
My aim to illustrate the above example is to drive home the argument that most companies don’t even do the most apparent things from a data analysis viewpoint.
The best way to start Analytics in your organization is to ask some simple and obvious questions, both from the shareholder/management and the clients. If you’ve got a list of questions and information you ‘d like to see, start using it to see if you can come up with certain information and answers to your questions.
The data science industry has many varied roles. An expert in data analysis, an expert in machine learning, an AI consultant, a data scientist, a computer engineer, etc. are only a few of the many positions you might get into. It will be easier to get into one job, based on your background and your work experience, than another. For example, if you are a software developer, moving to data engineering wouldn’t be hard for you. So, you’ll remain confused about the path to take and skills to hone until and unless you ‘re clear what you want to become.
What to do if the distinctions are not visible or you are not sure what you will become? I have just a few things I would suggest:
Speak to individuals who are already employed in data science companies to find out what positions are available and what each of them entails.
Take mentoring from people – ask them for a short time, and ask relevant questions. I ‘m sure nobody will hesitate to support an in-need citizen!
Figure what you want and what you’re good at, and select the role that suits your study area.
Here’s a descriptive comparison of what it is like to be a data scientist vs. Data Engineer vs. Statistician done a few months back. I ‘m sure it’ll help you come to a decision.
The next step is to convert the facts into reports that can be generated for various time intervals and data slices and dices. You’ve already started building a BI system in place after you have done so. If you’ve got a collection of reports that show relevant and engaging business information and provide feedback and responses to questions that any boss would love to know about, you’ve already built a case for yourself to continue using Analytics in your work/organization.
Make a case study of your work and show the top management case. Otherwise, add it to your CV. If your organization does not support your Analytics initiative, look outside in the domain concerned.
For a person with your new-found skills, there will be plenty of opportunities outside!
Read loads on Analytics – Enter forums on Analytics, Threads on Analytics, track Analytics companies and keep up with the latest Analytics events. It will keep you well-placed to track how Analytics is being implemented in various business contexts and functions, which will improve your field awareness.
Here are five possible career paths that you can choose in Analytics:
Now that you know what position you want to play and get ready for it, the next important thing you need to do is join a group of peers. What does it matter? It is because you’re kept motivated by a peer group. If you do it alone, taking up a new area can seem overwhelming, but when you have friends next to you, the job seems a little simpler.
The best way to be in a peer group is to have a group of people that you can communicate with physically. Otherwise, you can either have a bunch of people sharing similar goals over the internet, such as joining a Massive online course and interacting with batch mates.
You will concentrate on the practical aspects of stuff you ‘re learning when taking courses and training. It would help you not only grasp the definition but also give you a better understanding of how it would be implemented.
A few things to do when you pursue a course:
To understand the applications, make sure you do all the exercises and assignments.
Consult on a few open data sets and bring your learning into practice. And if you initially don’t grasp the logic behind a strategy, consider the premises, what it does, and how to view the outcomes. At a later point, you can still develop a deeper understanding.
Look at the ideas coming from people who have served in the sector.
To never stop learning, all and every source of knowledge you can find must be engulfed. Blogs run by most prominent Data Scientists are the most valuable source of this knowledge. These data scientists are very involved, updating the followers on their results and posting regularly about the recent progress in this area.
Every day, learn about data science and make up a habit of being updated with the latest events. But there may be many resources to follow, influential data scientists, and you have to be sure you are not following the wrong practices. So, following the proper resources is quite critical.
People in data science roles do not usually associate communication skills with rejection. They hope to ace the interview if they are technically profound. Probably that’s a myth. Have you ever been refused in an interview, where the interviewer said thank you after hearing your introduction?
Try this activity once; hear your intro and ask for honest feedback from your friend with excellent communication skills. He’ll show you the mirror, sure!
When you’re working in the field, communication skills are even more critical. You should know how to connect effectively if you want to share your thoughts with a colleague or prove your point in a meeting.
Initially, all of your attention will be on learning. Doing too much at the initial stage will ultimately push you to the point that you’re going to give up.
Gradually, once you’ve got the hang of the market, you can attend industry events and conferences, important meetings in your area, and take part in hackathons in your area – even if you just know a little. You never know who will help you out, and when and where!
A meeting in the data science community is constructive when it comes to making your mark. You get to meet people in your region who work actively in the industry, which can help you advance your career significantly by providing you with networking opportunities and developing a friendship with them. Connection to networks could:
Offer you inside details on what happens in your field of interest help you get mentoring support
Help you look for a job, and it will either be job searching tips directly by leads or future work opportunities.
As technology continues to develop at a rapid rate, the skills required to work with that technology need to evolve more rapidly or become obsolete. I mean that candidates must always be upset and self-educated to keep up-to-date on what happens in the industry after completing a formal qualification.
One particular work that has risen in significance rapidly in recent years is that of a data scientist. The need for more analytical and highly skilled people to interpret and mine that data for business comes with the rise of big data.
As with any group, participating in the group is crucial before you consider sharing your content. Read content submitted by other members, add valuable and relevant comments, and follow community rules. People don’t seem to like people who only concentrate on their interests.