With the passage of time, the concept of data science has changed. It was first used in the late 1990s to describe the process of collecting and cleaning datasets before applying statistical methods to them. Data analysis, predictive analysis, data mining, machine learning, and much more are now included. To put it another way, it might look like this:
You have the information. This data must be important, well-organised, and ideally digital in order to be useful in your decision-making. Once your data is in order, you can begin analysing it and creating dashboards and reports to understand your company’s performance better. Then you turn your attention to the future and begin producing predictive analytics. Predictive analytics allows you to evaluate possible future scenarios and forecast consumer behaviour in novel ways.
Now that we’ve mastered data science fundamentals, we can move on to the latest methods available. Here are a few to keep an eye out for:
Top 10 Data Science Techniques
Assume you’re a sales manager attempting to forecast next month’s sales. You know that dozens, if not hundreds, of variables, can influence the number, from the weather to a competitor’s promotion to rumours of a new and improved model. Maybe someone in your company has a hypothesis about what will have the greatest impact on sales. “Believe in me. We sell more the more rain we get.”
“Sales increase six weeks after the competitor’s promotion.” Regression analysis is a mathematical method of determining which of those has an effect. It provides answers to the following questions: Which factors are most important? Which of these can we ignore? What is the relationship between those variables? And, perhaps most importantly, how confident are we in each of these variables?
The process of identifying a function that divides a dataset into classes based on different parameters is known as classification. A computer programme is trained on the training dataset and then uses that training to categorise the data into different classes. The classification algorithm’s goal is to discover a mapping function that converts a discrete input into a discrete output. They may, for example, assist in predicting whether or not an online customer would make a purchase. It’s either a yes or a no: buyer or not buyer. Classification processes, on the other hand, aren’t limited to only two groups. For example, a classification method might help determine whether a picture contains a car or a truck.
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3. Linear regression
One of the predictive modelling methods is linear regression. It’s the relation between the dependent and independent variables. Regression assists in the discovery of associations between two variables.
For example, if we are going to buy a house and only use the area as the key factor in calculating the price, we are using simple linear regression, which is based on the area as a function and attempts to decide the target price.
Simple linear regression is named after the fact that only one attribute is taken into account. When we consider the number of rooms and floors, there are many variables to consider, and the price is determined based on all of them.
We call it linear regression since the relationship graph is linear and has a straight-line equation.
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4. Jackknife regression
The jackknife method, also known as the “leave one out” procedure, is a cross-validation technique invented by Quenouille to measure an estimator’s bias. A parameter’s jackknife estimation is an iterative method. The parameter is first calculated from the entire sample. Then, one by one, each factor is extracted from the sample, and the parameter of interest is determined using this smaller sample.
This type of calculation is known as a partial estimate (or also a jackknife replication). The discrepancy between the entire sample estimate and the partial estimate is then used to compute a pseudo-value. The pseudo-values are then used to estimate the parameter of interest in place of the original values, and their standard deviation is used to estimate the parameter standard error, which can then be used for null hypothesis testing and calculating confidence intervals.
5. Anomaly detection
In certain words, suspicious behavior in the data can be observed. It might not always be apparent as an outlier. Anomaly identification necessitates a more in depth understanding of the Data’s original behavior over time, as well as a comparison of the new behavior to see whether it fits.
When I compare Anomaly to Outlier, it’s the same as finding the odd one out in the data, or data that doesn’t fit in with the rest of the data. For example, identifying customer behavior that differs from that of the majority of the customers. Every outlier is an Anomaly, but every Anomaly isn’t necessarily an Anomaly. Anomaly Detection System is a technology that utilizes ensemble models and proprietary algorithms to provide high-level accuracy and efficiency in any business scenario.
Remember when seeing your name in the subject line of an email seemed like a huge step forward in digital marketing? Personalisation — supplying consumers with customised interactions that keep them engaged — now necessitates a much more rigorous and strategic strategy, and it’s crucial to staying competitive in a crowded and increasingly savvy sector.
Customers today gravitate toward brands that make them feel like they are heard, understood, and care about their unique wants and needs. This is where customisation comes into play. It allows brands to personalise the messages, deals, and experiences they deliver to each guest based on their unique profile. Consider it a progression from marketing communications to digital interactions, with data as the foundation. You can create strategies, content, and expe
riences that resonate with your target audience by gathering, analysing, and efficiently using data about customer demographics, preferences, and behaviours.
7. Lift analysis
Assume your boss has sent you some data and asked you to match a model to it and report back to him. You’d fitted a model and arrived at certain conclusions based on it. Now you find that there is a community of people at your workplace who have all fitted different models and come to different conclusions. Your boss loses his mind and throws you all out; now you need something to show that your findings are true.
The hypothesis testing for your rescue is about to begin. Here, you assume an initial belief (null hypothesis) and, assuming that belief is right, you use the model to measure various test statistics. You then go on to suggest that if your initial assumption is accurate, the test statistic should also obey some of the same rules that you predict based on your initial assumption.
If the test statistic deviates greatly from the predicted value, you can assume that the initial assumption is wrong and reject the null hypothesis.
8. Decision tree
Having a structure resembling a flowchart, in a decision tree, each of the nodes represents a test on an attribute (for example, if a coin flip would come up as tails or heads or), every branch represents a class mark (verdict made after the computing of all the attributes). The classification rules are defined by the paths from the root to leaf.
A decision tree and its closely related impact diagram are used as an analytical, as well as visual decision support method in decision analysis to measure the expected values (or expected utility) of challenging alternatives.
9. Game theory
Game Theory (and mechanism design) are highly useful methods for understanding and making algorithmic strategic decisions.
For example, a data scientist who is more interested in making business sense of analytics may be able to use game theory principles to extract strategic decisions from raw data. In other words, game theory (and, for that matter, system design) has the potential to replace unmeasurable, subjective conceptions of strategy with a quantifiable, data-driven approach to decision making.
The term “segmentation” refers to the division of the market into sections, or segments, that are definable, available, actionable, profitable, and have the potential to expand. In other words, a company would be unable to target the entire market due to time, cost, and effort constraints. It must have a ‘definable’ segment – a large group of people who can be defined and targeted with a fair amount of effort, expense, and time.
If a mass has been established, it must be decided if it can be effectively targeted with the available resources, orif the market is open to the organization. Will the segment react to the company’s marketing efforts (ads, costs, schemes, and promotions), or is it actionable by the company? Is it profitable to sell to them after this check, even though the product and goal are clear? Are the segment’s size and value going to increase, resulting in increased revenue and profits for the product?
Experts in data science are required in almost every industry, from government security to dating apps. Big data is used by millions of companies and government agencies to thrive and better serve their clients. Careers in data science are in high demand, and this trend is unlikely to change anytime soon, if ever.
If you want to break into the field of data science, there are a few things you can do to prepare yourself for these demanding yet exciting positions. Perhaps most importantly, you’ll need to impress potential employers by showing your knowledge and experience. Pursuing an advanced degree programme in your field of interest is one way to acquire those skills and experience.
We have tried to cover the ten most important machine learning techniques, starting with the most basic and working my way up to the cutting edge. Studying these methods thoroughly and understanding each one’s fundamentals can provide a solid foundation for further research into more advanced algorithms and methods.
There is still a lot to cover, including quality metrics, cross-validation, the class disparity in classification processes, and overfitting a model, to name a few.
If you want to explore data science, you can check the Executive PG Programme in Data Science course offered by upGrad. If you are a working professional, then the course will suit you best. More information regarding the course can be explored on the course website. For any queries, our team of assistance is ready to help you.