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How To Successfully Approach A Data Science Problem?
Over the past few years, the outbreak of data usage has made data become the new gold for companies across various domains. As per the recent surveys over 85% of the companies are trying to be data-driven, and the global market for Data Science is expected to reach $128.21 billion by 2022, up from $19.75 billion in 2016.
There’s no wonder why Data Science is the latest buzzword. But still, there are several companies that are struggling to reorganize the true potential of data driven decision making by implementing a coherent data strategy. The problem certainly isn’t lack of data. The problem lies in the ability of these companies in transforming their data into actionable insights for varied benefits. They majorly lack in the approach towards successfully handling a Data Science problem.
What Exactly Is A Data Science Problem?
A Data Science problem may
- Categorize or group data
- Identify patterns
- Identify anomalies
- Show correlations
- Predict outcomes
Let’s make it clear that a typical Data Science problem must be clearly specific and conclusive. Relatively a vague & immeasurable problem may not be ideal to attain a Data Science solution.
- Defining The Data Science Problem & Deciding On An Approach-
Determining the type of Data Science problem one is dealing with is a must. This is intermediate step for deciding which type of Machine Learning algorithms can be effectively applied for the best results.
Machine Learning problems generally fall into one of three types
Supervised-Predicts future outputs based on labeled input & output data
Unsupervised- Finds hidden patterns or groupings in unlabeled input data
Reinforcement Learning- It is about taking the decisions sequentially
Different Use Cases For Each Type Of Machine Learning Problem-
Unsupervised- Market Segmentation, Political Polling, Retail Recommendation Systems & many more
Classification (Supervised)- Medical Imaging, Natural Language Processing, And Image Recognition & more
Regression-Weather Forecasting, Voter Turnout, Home Sale Pricing & more
- Collect Data & Cleaning The Data
Once the problem is clearly defined then the immediate step is to collect the data & then clean it so as to make it ready for analysis.
- Analyze The Data & Interpret The Results
After collecting & cleaning the data the next step that follows is analyzing & interpreting the results, For analyzing the data most of the Data Scientists prefer Machine Learning algorithms.
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