Before we take a look into the key immersive data scientist salary factors, it’s important to note that geography has an enormous influence on the income of data scientists. According to glassdoor.com, a data scientist in the United States earns an average salary of almost $118000. However, the figure is about $93000 according to payscale.com. It goes without saying that data scientists with high experience can expect to earn high salaries. When beginning his/her career as a fresher, a data scientist can look forward to earn almost $90000 as an annual salary. Those having 5 to 10 years of experience can earn a take-home pay of about $109000. Data scientists with over 10 years of experience can expect to get an annual salary of $124000.

If you wonder how geographical location influences the pay of data scientists, some figures from the US would make the picture clear. Data scientists in San Jose earn 28% more than the national average, while it’s 18% more for those in Palo Alto. Compared to the national average, San Francisco pays 21% higher salary to data scientists, while for New York, the figures stand at 6% higher.

Some say the coveted job of data scientist may be losing some of its attractive quotients as salaries for the position has started to plateau. They say with growing competition and flattening salaries, the prospects that ere once stellar may no longer be so for data scientists. But despite a slowdown in the field, the position of data science is still a lucrative one, much more than many other posts in the IT landscape, which still makes it a coveted one.

If you have your eyes set on the domain of data science, you should know about the three key immersive data scientist salary factors. You may call these three areas that you need to work upon to become a better data scientist, and thus, earn a fat pay packet. But before we go further, it’s important to know for aspiring data scientists that they don’t need to know everything to become a successful data scientist. You should plan your career realistically. After all, there is an almost unlimited number of machine learning/data science topics, but you can actually learn only a handful to begin with. Despite what some self-proclaimed experts and unrealistic job applications may say, you don’t need to possess complete knowledge of every algorithm or have 5 to 10 years of work experience to become a practicing data scientist. So, instead of feeling overwhelmed by the huge number of topics you believe you must learn, you should ideally start with the basics and build upon it from there.

So, let’s delve deeper to take a closer look at each of these three key immersive data scientist salary factors.

1- Software Engineering

When you take an academic approach to data science, you may develop the inclination of writing code that only runs once. Additionally, you may have a tendency of developing difficult-to-read code without a consistent style, having a lack of documentation, and hard-coding particular values. Such practices indicate your solitary primary objective – to create a data science solution that works just a single time for a particular dataset.

An instance could be when you work with data that initially came in 10-minute intervals. Since you didn’t think about making your code easy to read or flexible to varying inputs, you would be at a loss when data starts coming in, say 5-minute increments. Had you written the code from a software engineering viewpoint, it must have been extensively tested with a lot of different inputs. Additionally, it would be well-documented, adhere to coding standards to make it easy for other developers to understand it, and function within an existing framework. What this means is that instead of writing code as a data scientist, you should approach the task as a software engineer.

Successful data scientists write code using software engineering best practices, thus ensuring their model is robust to be deployed and fits within an architecture. If you wonder how you can do it, you should know that nothing beats practice for learning technical skills. If your present job gives you the chance to do it, you can easily learn and hone your coding skills with practice. If not, you can take up collaborative open-source projects. To work out solid coding practices, you may even read through the source code for GitHub’s popular libraries. Finding a community of software engineers and data scientists, who are more experienced than you and from whom you can get advice, is yet another efficient way.  Using these modes, you may end up learning a number of practices including:

• Following a coding style guide
• Writing unit tests
• Thorough documentation of code
• Writing functions that accept varying parameters
• Refactoring code to make it easier and simpler to read
• Getting the code reviewed by others

Additionally, you may use linting tools, which are available in plenty and help you to check if your code follows a coding style. You could even focus more on writing efficient implementations rather than using brute force methods (such as opting for vectorization in place of looping). But it’s important for you to realize that you can’t change everything overnight or all at once. Thus, the key is to focus on a small number of practices and make them habits integrated into your workflows.

Now that you know about one of the key immersive data scientist salary factors, let’s move onto the next one.

2- Scaling Data Science

One difficulty you will face in the domain of data science is scaling a predictive model or an analysis of large datasets. Like many others, you may not have access to a computing cluster and won’t want to shell out a large sum for a personal supercomputer. To solve this problem, you may tend to apply the new methods you learn to small, well-behaved datasets. The only problem is that real-world datasets don’t have cleanliness limits or adhere to an exact size, which makes it important to use different approaches to resolve problems.

One solution is to use a remote instance, like through AWS EC2, or even multiple machines. However, this would mean learning ways to link to remote machines and mastering the command line (because you won’t have access to a GUI and your mouse on your EC2, for instance).

Another problem you may encounter is when handling larger datasets the memory of the machine. One way is iterating through a dataset one portion at a time, by breaking one large dataset into several smaller pieces, and using tools like Dask or Spark with PySpark to run the subsets through a parallel pipeline. You won’t need a cluster or a supercomputer  for this approach as you can parallelize processes on a personal machine using multiple cores. Once you have access to additional resources, you can adjust the same workflow to scale up.

3- Deep Learning

With the increasing use of AI across various industries, deep learning, which uses multi-layered neural networks, is going to be big, say some experts. Your present position may not require you to learn and apply deep learning as you can deal with problems by using conventional machine learning techniques such as Random Forest, etc. However, you should know that not every dataset that you need to work upon will be structured in neat columns and rows, and when they aren’t, neural networks would be your best bet, especially for handling projects with images or texts. Thus, familiarity with deep learning and being confident in implementing some of the techniques would let you handle a wider range of problems, which in turn would make you better placed to earn a higher salary as a data scientist. No wonder why deep learning features among key immersive data scientist salary factors.

Final words

Perhaps you now understand that key immersive data scientist salary factors are as much about your present skills and competence as well as your inclination to pick up new skills and techniques that you may not need now but can apply to future situations to solve problems. So, act accordingly to ensure these three key immersive data scientist salary factors as mentioned above work in your favor.

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To learn more about data science, click here and read our another article.

The post Data Scientist Salary Is Immersive With 3 Steps first appeared on Magnimind Academy.

Undeniably, a career as a data scientist is highly lucrative and rewarding, but you need to stay on top of the game to enjoy those results. Data scientists are often considered a rare breed by many because of the mix of skills they possess. In order to make sense of the massive amount of data captured on a regular basis and utilize it to solve critical business problems, identify trends, and make decisions that can support new ideas, businesses need professionals with a mix of coding, databases, data visualization, statistics, data  preparation skills, and machine learning.

To become an effective data scientist, you need to have robust data skills along with a great practical skillset. You probably have already heard what data science experts often say: 80 percent of a data scientist’s job involves data cleaning. You should understand that data science isn’t all about doing predictive analytics and machine learning 24/7. Instead, you’d need to employ your data skills first to complete different steps before you can run a proper machine learning algorithm. These steps usually include data collection, data formatting, data cleaning, transforming the data to a proper format, discovering as well as understanding the data, running various data analytics projects, data visualization, and more. In other words, having polished data skills is an extremely important aspect of any successful data scientist.

In this article, we’re going to look at different ways in which a data scientist can polish his/her data skills.

Key tips to improve data skills

HAVING A CLEAR UNDERSTANDING OF THE BUSINESS CASE

When a data scientist is presented with a problem, in most of the cases, the initial time is spent on finalizing and identifying the means to attain the ultimate goal instead of focusing on the goal itself. Without a clear understanding of the business case, the probabilities for a data scientist to come up with a solution that doesn’t meet the client expectation is higher. Hence, there’s a great need for data scientists to clearly understand both the business case and client expectations before they decide a course of action.

CHARTING THE HYPOTHESIS

There’re always probable outcomes to every probable question and that’s something every data scientist needs to consider. Hence, it’s very crucial for every data scientist to understand possible loopholes, chart the possible outcomes, and develop a solution in accordance with those.

OBSERVING THE TRENDS

Having a clear understanding of the particular industry where a data scientist is working and following the recent trends can help him/her to identify the business drivers. Hence, every data scientist should make it a frequent practice to follow internal trends in his/her day-to-day work. Understanding the unique perspective and information provided by the functions and defining their impact on the business level strategic thinking are one of the key steps to improve data skills.

Here’re the key areas on which every data scientist should focus on to polish his/her overall data skills.

NUMERACY SKILLS

These involve mathematical skills that include an array of abilities like having a good understanding of numbers and figures, understanding the existing relationships between numbers, interpreting any mathematical information, having the ability to organize information, knowing how to measure as well as analyze data, having calculation skills etc.

ANALYTICAL SKILLS

These skills refer to a data scientist’s ability to capture, view as well as analyze all sorts of information in details. They also refer to the ability to view a situation or challenge from different perspectives. Analytical skills are crucial data skills which make it possible for a data scientist to address business problems by forming decisions in the most useful way. Hence, to become a successful data scientist, you’ve to acquire and polish your analytical thinking and skills.

ATTENTION TO DETAILS

This is one of the crucial data skills that any data scientist needs to develop. Ability to pay complete attention to details enables a data scientist to identify initially unseen links and details. This ability is particularly important when it comes to solving problems and making decisions. Also, one with this ability tends to perform better and stands a lower possibility of making errors.

Here’re the subjects that every data scientist should try to master to boost his/her data skills.

DATA MINING

Data mining, which refers to the process of analyzing massive datasets to develop insights and identify patterns, is experiencing high demand as more companies and industries seek to make sense of the captured data and predict efficient outcomes. This is one of those data skills that not only help big businesses but every company where correlation and patterns matter.

NATURAL LANGUAGE PROCESSING

Natural language processing has the potential to transform any business that depends heavily on human interaction. Teaching a machine to understand the complexities of human language is a highly difficult process that needs specialized skills. Data scientists with a robust analytical background should focus on this field to remain in-demand.

MACHINE LEARNING

The subfield of AI involves computer systems utilizing algorithms and data to teach themselves to come up with predictions without being programmed. Machine learning is being heavily used in advanced technologies like personalizing the consumer experience, self-driving cars, among others. The field is a great combination of data skills, software engineering, mathematics etc and thus it requires an array of skillsets to become an expert. Data scientists looking to improve their skills in this field can join communities of machine learning engineers and data scientists where members work together to build models, publish datasets, and compete to solve different data science problems.

PYTHON

Python has become one of the fastest-growing and widely used programming languages in recent years. It’s also a powerful data visualization tool that comes with a set of libraries which include some specific to machine learning like SciPy, NumPy, Pandas, and scikit-learn. Data scientists can improve their Python skills either by learning on their own with the help of online tutorials or by joining a coding bootcamp.

R

This open-source statistical software package helps data scientists to simplify the analysis of massive datasets and come with features like clustering, linear and non-linear modeling, time-series analysis etc. R is gaining steady popularity along with Python and is one of the most crucial skills in the data science domain. R also enables data scientists to perform predictive and statistical analysis on real-time data, and develop visuals to communicate the findings to the business side. Again, data scientists can either join a coding bootcamp or take the self-learning route.

HADOOP

This software framework helps to store and process huge volumes of data across different clusters of computing devices. It’s flexible, scalable, and helps businesses to identify trends and predict results to better decision-making. While a data scientist can get a job with limited Hadoop knowledge, a robust knowledge of the framework is a solid selling point which may lead to more opportunities.

SQL

SQL or Structured Query Language is a domain-specific programming language which helps data scientists to retrieve data and gives them a way to access as well as manipulate huge amounts of information found in relational database management systems. SQL commands have the ability to capture and break down data, and edit database indexes and tables to improve accuracy. There’re some interactive tools available on the internet that lets programmers test and share queries.

DATA VISUALIZATION

Businesses and organizations are generating a huge amount of data every day. Data scientists have to be able to translate this data into a format which is simple to understand in order to convert the data. Graphical representation and pictorial formats make it much easier for common people to understand the findings. Data visualization tools like Tableau, ggplot2, RapidMiner etc are used to ease this task. Hence, data scientists need to focus on improving this skill.

STATISTICS

Proficiency in mathematics and calculations is a fundamental need for any data scientist to be able to perform tasks that involve crucial data skills. Hence, it’s essential to have a solid understanding of statistics as well as statistical analysis. In case one fails to have adequate knowledge of the core statistical concepts, it can become highly difficult to understand how statistical modeling works. To improve statistical skills, data scientists can start with basic statistics, inferential, and descriptive statistics.

Final Takeaway

When you’re trying to become a successful data scientist, you shouldn’t only focus on improving your machine learning, deep learning or any other specialized skills. Instead, you should work on polishing the above areas and skills to improve your overall data skills. Being a master of machine learning or deep learning surely sounds exciting, but if you’ve just entered the field of data science, you should focus on polishing your data skills which are crucial for advancing to the next level.

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To learn more about data science, click here and read our another article.

The post How Data Scientists Can Improve Data Skills? first appeared on Magnimind Academy.