“The corporations that embrace Data Science will find their long-term, generational growth directly proportional to how effectively they incorporated data scientist teams into their strategic planning.”
As the world entered the era of overload with big data, being created every second of the day, the need for its storage also grew. It has become essential for companies to make sense of it in order to make successful business decisions. Security and proper data management of data are also paramount. It is ensuring that vital data is never lost and is protected inside the organization. Securing data is the most crucial aspect of data management as it offers undeniable security to both the organization and its workforce.
What is the role of a Data Manager?
As a Data Manager, you will analyze the data needs of the company or research organization and use skills in coding to maintain secure databases. Then you will collect and organize the data obtained. Data management is an analytic profession that strives to serve the goal of deciphering the best in a data ecosystem and enforcing it for better performance and efficiency. Within your job roles, you will see increments in responsibilities, including the strategic development of a company’s data system to ensure that the information travels in between the corporation flawlessly.
Some of your responsibilities as a data manager will look like the following:
- Developing and implementing necessary procedures for efficient data management.
- Creating legislations for smooth data distribution.
- Managing staff for the day-to-day use of a data ecosystem.
- Monitoring and evaluation of information with the data system that can affect the overall analytics.
- Assessment of software and hardware performance and creating solutions for deficiencies and improvement of the same.
- Recognizing and reinforcing the integrity of the chose a digital security system to protect exclusive and critical information.
What is it like being a Data Scientist?
You will be working on fascinating problems in the realm of data science and AI, trying to find new solutions all the time. You will be using your creative juices to the fullest, working with exciting people. As a Data Scientist, you will work with data analytics programs and algorithms to extract meaning from data, often using high-performance computers to do so.
What is the difference between Data Manager and Data Scientist?
A Data Scientist looks at the land, reading the natural signs, and making associations between different elements to find ways of hitting a gold-bearing vein. A Data Manager is making sure the gold extraction operation is running smoothly.
The clear difference between a data scientist and data manager is certainly a grey area. Activities relevant to data management are concerned with the governance of data. It deals with the development of policies, programs, plans, and practices that are concerned with the protecting, controlling, and upgrading the general value of data assets.
Data science on the other hand derives the applications from data management. As a data scientist, you will be concerned with finding logical application to the bigger set of data.
You will be constantly hunting for insights and answers to questions that were not present previously.
How can I become a good Data Manager?
Learn to love data
Data science is a vast and fuzzy field, which makes it hard to learn. Without adequate motivation, you’ll end up stopping halfway through and believing you can’t do it. You must love exploring data and spending hours on it.
Learn by doing
The best way to master something is by practicing it. By working on your projects, you gain skills that are immediately applicable and useful. You also have a nice way to build a portfolio. One method to start projects is to find a dataset you like. The following datasets can be highly beneficial to your journey:
- 100+ Interesting Data Sets for Statistics – rs.io
- Datasets Archive • /r/datasets
Learn about image recognition, neural networks, and other cutting-edge techniques
Although data science doesn’t involve any of it will help you with your work. Here are some essential guidelines:
- 90% of your work will be data cleaning.
- Being a specialist with a few algorithms is better than knowing a little about many. Study linear regression, clustering, and logistic regression and explain and interpret their results. If you can complete a data project from start to finish using them, you’ll be much more employable than if you know every single algorithm but can’t use them.
- Most of the time, when you use an algorithm, it will be a version from a library (you’ll rarely be coding your SVM implementations – it takes too long).
Learn to communicate results
Data scientists constantly need to present the development and journey of their work to others. Mastering, this is a way to be a great data scientist. Here are some ideas on how to do it:
Learn from peers
It’s incredible how much you can learn from working with others.
Some ideas here:
- Find people to work with at meetups.
- Contribute to open-source packages.
- Try out Kaggle and see if you can find a teammate.
Constantly raise the bar of your skills
Data science is a steep mountain to climb, and if you stop climbing, it’s easy to fail.
Go outside your comfort zone:
- Work with a larger dataset. Learn to use a spark.
- See if you can make your algorithm faster.
- How would you scale your algorithm to multiple processors? Can you do it?
- Try to teach a novice to do the same things you’re doing now.
Get a Master or bachelor’s degree in data science
Getting a bachelor’s degree in data management is a simple and straightforward channel to enter the sector. Degrees in computer science, computer engineering, statistics, or business administration can also be useful for this field.
Get acquainted with these resources:
- Khan Academy — useful basic statistics and linear algebra content.
- Introduction to Linear Algebra, 4th Edition — Great linear algebra book by Gilbert Strang.
- Textbook | Calculus Online Textbook | MIT OpenCourseWare — also by Gilbert Strang, great calculus book.
- Data mining, inference, and prediction. 2nd Edition — Elements of statistical learning, a good machine learning book.
- Andrew Ng’s Online Machine Learning Class — the original coursera class.
- OpenIntro Statistics — Good basic stats book.
- You can find online training in Data Science like Simplilearn, Edureka, UpGrad, Learnbay.
Which degree is best for Data Science?
The top 10 colleges in Australia are offering a program in Master of Data Science and Data Management.
A Master’s in Data Science has become the top choice for graduates. The world is overloaded with data, and we need specialists to deal with it. This is where a candidate with a Master’s in Data Science becomes essential.
- The University of Melbourne: Ranked number one in Australia for Computer Science and Statistics, The University of Melbourne is a top choice for the Master of Data Science program.
- The University of Sydney,
- RMIT University, Melbourne
- Monash University
- La Trobe University
- The University of Adelaide
- University of New South Wales
- Deakin University
- The University of Queensland
- James Cook University
The top colleges in Australia offering a Bachelor of Data Science:
- University of South Australia Online, Adelaide – Bachelor of Information Technology and Data Analytics
- University of Southern Queensland (USQ) – Bachelor of Information Technology (Data Analytics)
- S P Jain School of Global Management Singapore, Sydney – Bachelor of Data Science
- RMIT, Melbourne – Bachelor of Data Science
Is Data Management a good career choice?
The field of Data Management is only going to get bigger and more advanced with time. Companies, however big or small, are all wanting to make sense of the data generated every second, every hour, in order to make the right decisions for the future. And they’re not enough professionals in the market to deal with this data. Demand for data scientists and data managers appears to have outstripped the supply of qualified applicants. Therefore, the current job market in Australia is quite promising for Data Scientists and Big Data Analytics professionals.
How much do the Data Scientists and Data Manager earn in Australia?
Data science and management is quite a buzzword among aspiring professionals and for a good reason. There is both monetary as well as objective satisfaction in the sector as a professional. Naturally, data science offers higher standards in the prospect of salaries. On average, a data scientist makes a total of $110,000 annually.
Salaries variate depending on your locations:
- Sydney – A$104.000
- Parramatta – A$149.00
- Melbourne – A$108.000
- Canberra – A$95.000
According to Indeed, The National average salary for a Data Manager in Australia is A$124.000. Salaries variate depending on your locations:
- Sydney – A$133.000
- Parramatta – A$121.00
- Melbourne A$135.000
- Canberra A$139.000