Become an expert in the exciting new world of Data Science with AI & Machine Learning, get trained in cutting edge technologies and work on real-life industry grade projects. Enroll In ACTE Data Science Course with Class Room & Online Training
Most of the peoples need good job and good salary while, Learning data science will raise your probabilities of acquiring a good job and the well maintained career option.... The demand for a data scientist is growing day by day since there are not many experts in this field. Learning data science will provide you the chance of finding a well decent job in this market where they are particularly required right now. Data Science a highly lucrative career option.
Meanwhile, For several years data scientist has been ranked as one of the top jobs in india and around the world, in terms of pay, job demand, and satisfaction. Companies are increasingly using the data scientist title for other similar roles such as data analyst. "I think that what we're seeing is a little bit of the standardization and the professionalization of data science," "The past ten years have been a bit of the Wild West when it comes to data science.
While, demand for data science skills is growing exponentially, according to job sites. The supply of skilled applicants, however, is growing at a higher pace. It's a great time to be a data scientist entering the job market. ... "More employers than ever are looking to hire data scientists." it's a great time to be a data scientist entering the job market. That's according to recent data from job sites Indeed and Dice.
We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings in data science. Moreover, we have a very active placement cell that provides 100% placement assistance to our students. The cell also contributes by training students in mock interviews and discussions even after the course completion.
Data science is a good field, skilled data science are some of the most sought-after professionals in the world. Because the demand is so high and strong, and the supply of people who can truly do this job well is so limited, data science is a command huge salary and excellent perks, some peoples can able even at the entry level. Many companies also label data analysts as information scientists. This classification typically involves working with a company’s proprietary database.
So coming to this there are not a great different. Data Science is the field that comprises of everything that related to data cleansing, data mining, data preparation, and data analysis. Big Data refers to the vast volume of data that is difficult to store and process in real-time. This data can used to analyse insights that can lead to better decision making. Data Science algorithms are used in industries such as internet searches, search recommendations, advertisements.
Analysts and researchers have been around long before big data, which is why data analyst roles are well defined. Data analysts do not need to have advanced coding skills, but have experience with analytics software, data visualization software, and data management programs.
Our courseware is designed to give a hands-on approach to the students in Data science. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
Data science Is giving world wide job opporutunites
Data science remains to be one of the best jobs in 2020. According to McKinsey, the US will be facing a shortfall of 250,000 data scientists by 2024. Though the jobs market is in constant change, a data scientist job still hits the top list. If you are interested in learning data science, that's awesome. With more and more things being driven by data we need more people to understand what's needed to produce successful and safe data science machine learning projects. ... Data science is totally worth doing.
Data Scientists Have a great future. Research shows 94 percent of data science graduates have gotten jobs in the field since 2011. One of the indicators that data science careers are well suited for the future is the dramatic increase in data science job. The demand for a data science is growing day by day since there are not many experts in this field. Learning data science will provide you the chance of finding a decent job and the bright future in this market where they are particularly required right now.
The potential for quantum computing and data science is huge in the future. Machine Learning can also process the information much faster with its accelerated learning and advanced capabilities. Based on this, the time required for solving complex problems significantly reduced. Companies require skilled Data Scientists to process and analyses their data. They not only analyse the data but also improve its quality. Therefore, Data Science deals with enriching data and making it better for their company
Understanding Data Science and Why It’s So Important
It’s been said that Data Scientist is the“sexiest job title of the 21st century.” Why is it such a demanded position these days? The short answer is that over the last decade there’s been a massive explosion in both the data generated and retained by companies, as well as you and me. Sometimes we call this “big data,” and like a pile of lumber we’d like to build something with it. Data scientists are the people who make sense out of all this data and figure out just what can be done with it.
At ACTE, our Data team is at the helm of generating robust, actionable analytics from immense data sets. It’s these efforts that bring clarity to how people interact with the web and are the basis for usable features that inform critical business strategies.
The demand for data scientists is increasing so quickly, that McKinsey predicts that by 2018, there will be a 50 percent gap in the supply of data scientists versus demand.
Great for us, but what is data science? What exactly do we do with all that data?
What is Data Science?
A data scientist is the adult version of the kid who can’t stop asking “Why?”. They’re the kind of person who goes into an ice cream shop and gets five different scoops on their cone because they really need to know what each one tastes like. Similarly, even the term data scientist is a catchall title that encompasses many different flavors of work.That’s the major differentiator between a data scientist and a statistician or an analyst or an engineer; the data scientist is doing a little of each of those tasks. Of course, what someone whose job title is data scientist will do at a given company depends on the company and the person, and may look more like one of those other titles, rather than a mixture of all three.
A data scientist is someone who does the following tasks:
- Data analysis
- Modeling/statistics
- Engineering/prototyping
The order of these tasks is intentional, and it roughly reflects the life cycle of a data science project. To be fair, we should add “0. Data cleaning” to that list, as it can be one of the most time consuming tasks of a data scientist. It’s also an incredible litmus test for data scientists. Someone who can’t parse a messy CSV isn’t going to cut it as a data scientist).
Let’s look at these tasks in more detail.
Data cleaning
- There’s lot’s of data out there, but much of it is not in an easy to use format. This part of a data scientist’s job involves making sure that data is nicely formatted and conforms to some set of rules.
- As an example, consider a CSV where each row describes the finances of a fast food franchise.
- There might be columns for city, state, and number of burgers sold in the last year. But, rather than having all this data in one document (that would be too easy, right?), it probably comes spread across many different files, which need to be joined together.
- Doing this is in some sense the easy part. The hard part is making sure the resulting combination makes sense.
- Typically there will be some formatting inconsistencies, and floating somewhere in the data set is a row where the number of burgers sold is ‘Idaho’ and the state is 25,000.
- Data cleaning is all about finding these hiccups, fixing them, and making sure they’ll be fixed automatically in the future. As an added bonus, all the downstream work from this point can only be as good as the data you’ve assembled.
Data analysis
- This is the sort of work most people think of using Excel for, but dramatically juiced up. A data scientist will typically work with data sets that are too large to open in a typical spreadsheet program, and may even be too large to work with on a single computer.
- Data analysis is the realm of visualization (tables are for robots). This is where you make lots of plots of the data in an attempt to understand it (plotting is also another place where spreadsheets start lagging behind).
- Through this process, a data scientist is trying to craft a story, explaining the data in a way that will be easy to communicate and easy to act on.
- Sometimes this can be something simple, like figuring out what property or event signals when new users convert into long-term users, or something more complex, like figuring out when someone is slowly scamming you for lots of money ala Office Space.
- For example, data scientists at Facebook figured out that having at least ten friends helps guarantee that a user will stay active on the site, which is why there is so much machinery on the site devoted to finding new friends.
Modeling/statistics
- Whether a data scientist thinks they’re doing modeling or statistics depends on their background. People who studied statistics consider themselves to be statisticians; everyone else is probably going to claim to be more of a modeler (or an expert in machine learning if they’re feeling fancy).
- We live in a golden age of machine learning, where very powerful algorithms are available as black boxes that produce good results. However, it’s easy to find yourself sitting on a problem that no model is going to work well on right out of the box.
- So a data scientist spends a lot of time evaluating and tweaking models, as well as going back to the data to bring out new features that can help make better models.
Engineering/prototyping
- Having clean data and a good model is only the tip of the iceberg. Going back to the visitor model in the last section, even if I’ve got a good model for predicting how many people visit a site (I’d like to think I do), it doesn’t do anyone much good if I can’t give those predictions to our customers and do it consistently. This means building some sort of data product that can be used by people who aren’t data scientists. This can take many forms: a visualization (or chart), a metric on a dashboard, or an application.
- Whether a data scientist is building a full on application or just a proof of concept often depends on how much data is involved, how snappy things need to be, and who the final consumers are going to be.
- We’re still in the early days of engineering with a slant towards projects that utilize large amounts of data, and so many of the tools and techniques that make general programming easier either aren’t available in the tools used by most data or don’t work quite as well in their new context (unit tests come to this data scientist’s mind).