A data science project requires numerous iterations that are time-consuming. When dealing with numbers and data interpretations, it goes without question that you have to be quite smart and proactive.

It’s not surprising that iterations can be frustrating if they require regular updates. Sometimes, the model is six months old that needs current information or other times you miss out on some data, so the analysis has to be done all over again. In this article, we will focus on the ways by which the Data Scientists and Business Analysts can increase productivity without spending so much time on unhealthy iterations.

Tips and Tricks for Data Scientists

Keeping the Bigger Picture in Mind

Long-term goals should be considered a priority when doing analyses. There could be many small issues cropping up that shouldn’t overshadow the bigger ones. Be observant in deciding the problems that are going to affect the organization on a larger scale. Focus on those bigger problems and look for stable solutions. Data scientists and business analysts have to be visionary to manifest solutions.

Understanding the Problem and Keeping the Requirements at Hand

Data science is not about implementing a fancy/complex algorithm or doing some complex data aggregation. Data science is more about providing a solution to the problem at hand. All the tools like ML, visualization, or optimization algorithms are just meant through which one can arrive at a suitable solution. Always understand the problem you are trying to solve. One should not jump directly to Machine Learning or statistics right after getting the data. We should analyze what data is about and what all you need to know and perform to come to the solution of your problem. Also, it is important to always keep an eye of the feasibility of the solution in terms of implementation. A good solution is always the one that is easily implementable. Always know what you need to achieve a solution to the problems.

More Real-World-Oriented Approach

Data science involves providing a solution to real-world use cases. Hence, one should always keep a real-world oriented approach. One should always focus on the domain/business use case of the problem at hand and the solution to be implemented rather than just purely looking at it from the technical side. Technical aspect focusses on the correctness of the solution, but the business aspect focusses on the implementation and usage aspect of the solution. Sometimes, you may not need a complex incomprehensive algorithm to meet your requirements, rather, you are happier with a simple algorithm that may not give as a correct result as the previous one, but its accuracy can be traded with its comprehensible attribute. Knowledge of technical aspect is a must.

Not Everything Is ML

Recently, Machine Learning has seen a great advancement in its application in various business applications. With great prediction capabilities, Machine Learning can solve many of the complex problems in various business scenarios. But one should note that data science is not about only Machine Learning. Machine Learning is just a small part of it. Data science is more about arriving at a feasible solution for a given problem. One should focus on areas like data cleaning, data visualization, and the ability to extensively explore the data and find relations between the various attributes. It is about the ability to crunch out meaningful numbers that matter the most. A good data scientist focusses more on all the above qualities rather than just trying to fit Machine Learning algorithms on the problem statements.

Programming Languages

It is important to have a grip on at least one programming language widely used in Data Science. There are plenty that can help you learn data science in Python and R. Either you should know R very well and some Python or Python very well but some R.

Data Cleaning and EDA

Exploratory Data Analysis is one of the important steps in the data analysis process. Here, the focus is on making sense of the data at hand — things like formulating the correct questions to ask to your dataset, how to manipulate the data sources to get the required answers, and others. This is done by taking an elaborate look at trends, patterns, and outliers using a visual method. Let us say you are cleaning data for language processing tasks, and simple models might give you the best result. Cleaning is one of the most complex processes in data science, since almost every data available or extracted for language processing tasks is unstructured. It is a fact that a highly processed and neatly structured data will yield better results than a noisy one. We should rather try to perform cleaning tasks with simple regular expressions rather than using complex tools

Always Open to Learning More and More

“Data Science is a journey, not a destination.” This line gives us an insight into how huge the data science domain is and why constant learning is as important as build intelligent models. Practitioners who keep themselves updated with the new tech being developed every day, are able to implement and solve business problems faster.  With all the resources available on the internet like MOOCs, one can easily make use of these to be updated. Also showcasing your skill on your blog or Github is an important hack which most of us are unaware of. This not only benefits their “The man who is too old to learn was probably always too old to learn.”

Evaluating Models and Avoiding Overfit

Separate the data into two sets — the training set and the testing set to get a stronger prediction of an outcome. Cross-validation is the most convenient method to analyze numerical data without over-fitting. It examines the out-of-sample fit.

Converting Findings Into Actions

Again, this might sound like a simple tip, but you see both the beginners as well as the advanced people falter on it. The beginners would perform steps in excel, which would include copy paste of data. For the advanced users, any work done through command line interface might not be reproducible. Similarly, you need to extra cautious while working with notebooks. You should control your urge to go back and change any previous step which uses the dataset which has been computed later in the flow. Notebooks are very powerful to maintain a flow. If we do not maintain the flow, it can be very tardy as well.

Taking Rest

When do I work the best? It’s when I provide myself a 2 to 3-hour window to work on a problem/project. You can’t multi-task as a data scientist. You need to focus on a single problem at a time to make sure you get the best out of yourself. 2 to 3-hour chunks work best for me, but you can decide yours.

Conclusion

Data science requires continuous learning and it is more of a journey rather than a destination. One always keeps learning more and more about data science, hence, one should always keep the above tricks and tips in his/her arsenal to boost up the productivity of their own self and are able to deliver more value to complex problems that can be solved with simple solutions! Stay tuned for more articles on data science.