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# Top 10 Latest Data Science Techniques You Should be Using in 2023

With the passage of time, the concept of data science has changed. It was first used in the lateĀ  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:Ā

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 toĀ  understand yourĀ  company’s performance better.Ā  Then you turn your attention to the future and begin producing predictive analytics. PredictiveĀ  analytics allows you to evaluate possible future scenarios and forecast consumer behaviour in novel ways.Ā Ā

NowĀ  that we’ve mastered data science fundamentals, we can move on to the latest methodsĀ  available. Here are a few to keep an eye out for:Ā

## Top 10 Data Science Techniques

### 1. Regression

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.Ā  It provides answers to theĀ  following questions: Which factors are most important? Which of these can we ignore? What isĀ  the relationship between those variables? And, perhaps most importantly, how confident are we in each of these variables?Ā

### 2. Classification

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 categoriseĀ  the data into different classes. TheĀ  classification algorithm’s goal is to discover a mapping function that converts a discrete inputĀ  into aĀ  discreteĀ  output. They may,Ā  for example, assist inĀ  predicting whetherĀ  orĀ  not anĀ  onlineĀ  customer would make a purchase.Ā  It’s either a yes or a no: buyer or not buyer. ClassificationĀ  processes, onĀ  the other hand, aren’t limitedĀ  to onlyĀ  two groups. For example, a classificationĀ  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 ofĀ  associations between two variables.Ā Ā

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 functionĀ  and attempts to decide the target price.Ā Ā

Simple linear regression is named after the fact that only one attribute is taken into account.Ā  When we consider the number of rooms and floors, there are many variables to consider, andĀ  the price is determined based on all of them.Ā Ā

We call it linear regression since the relationship graph is linear and has a straight-line equation.Ā

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### 4. Jackknife regression

The jackknife method, also known as the “leave one out” procedure, isĀ  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 ofĀ  interest is determined using this smaller sample.

This type of calculation is known as a partialĀ  estimate (or also a jackknife replication). The discrepancy between the entire sample estimateĀ  and the partial estimate is then used to compute a pseudo-value. The pseudo-values are thenĀ  usedĀ  to estimateĀ  the parameter of interest in place ofĀ  the original values, andĀ  their standardĀ  deviation is usedĀ  to estimateĀ  the parameter standard error, which canĀ  then be usedĀ  for nullĀ  hypothesis testing and calculating confidence intervals.Ā

### 5. AnomalyĀ  detection

InĀ  certainĀ  words,Ā  suspiciousĀ  behavior inĀ  theĀ  dataĀ  canĀ  beĀ  observed.Ā  ItĀ  mightĀ  notĀ  alwaysĀ  beĀ  apparentĀ  asĀ  anĀ  outlier.Ā  AnomalyĀ  identificationĀ  necessitatesĀ  aĀ  moreĀ  in depth understanding of the Data’s original behavior over time, as well as a comparison of theĀ  new behavior to see whether it fits.Ā Ā

WhenĀ  I compare AnomalyĀ  to Outlier, it’sĀ  the same as findingĀ  the odd one out inĀ  the data, orĀ  dataĀ  that doesn’t fit in withĀ  the rest ofĀ  the data. For example, identifying customer behaviorĀ  that differs from that of the majority of the customers. Every outlier is an Anomaly, but everyĀ  Anomaly isn’t necessarily an Anomaly. Anomaly Detection System is a technology that utilizesĀ  ensemble models and proprietary algorithms to provide high-level accuracy and efficiency inĀ  any business scenario.

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### 6. Personalisation

Remember when seeing your name in the subject line of an email seemedĀ  like aĀ  hugeĀ  stepĀ  forward inĀ  digital marketing? Personalisation āĀ  supplyingĀ  consumers withĀ  customised interactionsĀ  that keepĀ  them engaged ā now necessitates a much more rigorousĀ  andĀ  strategicĀ  strategy,Ā  andĀ  it’sĀ  crucialĀ  toĀ  stayingĀ  competitiveĀ  inĀ  aĀ  crowdedĀ  andĀ  increasinglyĀ  savvy sector.Ā Ā

Customers today gravitate toward brands that make them feel like they are heard, understood,Ā  and care about their unique wants and needs. This is where customisation comes into play. ItĀ  allows brands to personalise the messages, deals, and experiences they deliver to each guestĀ  basedĀ  onĀ  theirĀ  uniqueĀ  profile.Ā  Consider it aĀ  progressionĀ  from marketingĀ  communicationsĀ  toĀ  digital interactions, with data as the foundation. You can create strategies, content, and experiences that resonate with your target audience by gathering, analysing, and efficiently usingĀ  data about customer demographics, preferences, and behaviours.Ā

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### 7. Lift analysis

Assume your boss has sent you some data and asked you to match a model to itĀ  and report back to him. You’d fitted a model and arrived at certain conclusions based on it.Ā  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;Ā  now you need something to show that your findings are true.Ā

TheĀ  hypothesisĀ  testingĀ  forĀ  yourĀ  rescue isĀ  aboutĀ  toĀ  begin.Ā  Here,Ā  youĀ  assumeĀ  an initialĀ  beliefĀ  (null hypothesis) and, assuming that belief is right, you use the model to measure various testĀ  statistics. You then go on to suggest that if your initial assumption is accurate, the test statisticĀ  should also obey some of the same rules that you predict based on your initial assumption.Ā Ā

IfĀ  theĀ  test statistic deviates greatlyĀ  fromĀ  the predicted value, you can assumeĀ  thatĀ  the initialĀ  assumption is wrong and reject the null hypothesis.Ā

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### 8. Decision tree

Having a structure resembling a flowchart, in a decision tree, each of the nodes represents a test on anĀ  attribute (for example, if a coin flip would come up as tails orĀ  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 rootĀ  to leaf.Ā Ā

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)Ā  of challenging alternatives.

### 9. Game theory

Game TheoryĀ  (and mechanism design) are highly useful methodsĀ  for understanding and making algorithmic strategic decisions.Ā Ā

ForĀ  example,Ā  aĀ  dataĀ  scientistĀ  whoĀ  isĀ  moreĀ  interestedĀ  inĀ  makingĀ  businessĀ  senseĀ  ofĀ  analyticsĀ  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 replaceĀ  unmeasurable, subjective conceptions of strategy with a quantifiable, data-driven approach toĀ  decision making.Ā

### 10. Segmentation

The term “segmentation” refers to the division of the market into sections, orĀ  segments,Ā  thatĀ  areĀ  definable,Ā  available,Ā  actionable,Ā  profitable,Ā  andĀ  haveĀ  theĀ  potentialĀ  toĀ  expand.Ā  InĀ  otherĀ  words,Ā  aĀ  companyĀ  wouldĀ  beĀ  unableĀ  toĀ  targetĀ  theĀ  entire marketĀ  dueĀ  toĀ  time,Ā  cost, and effort constraints. It must have a ‘definable’ segment – a large group of people whoĀ  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 marketingĀ  effortsĀ  (ads,Ā  costs,Ā  schemes, and promotions), or is it actionable by theĀ  company?Ā  Is it profitable to sellĀ  to themĀ  afterĀ  thisĀ  check,Ā  evenĀ  thoughĀ  theĀ  productĀ  andĀ  goalĀ  areĀ  clear? Are the segment’s size and value going to increase, resulting in increased revenue and profits for theĀ  product?Ā

Experts inĀ  dataĀ  scienceĀ  areĀ  required inĀ  almostĀ  every industry,Ā  fromĀ  governmentĀ  securityĀ  to dating apps. Big data is used by millions of companies and government agencies to thrive andĀ  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. PerhapsĀ  most importantly, you’ll need to impress potential employers by showing your knowledge andĀ  experience.Ā  Pursuing an advancedĀ  degreeĀ  programme inĀ  your fieldĀ  of interest isĀ  one wayĀ  toĀ  acquire those skills and experience.Ā

We have tried to cover the ten most important machine learningĀ  techniques, starting withĀ  the mostĀ  basic and working my way up to the cuttingĀ  edge.Ā  StudyingĀ  theseĀ  methodsĀ  thoroughly and understanding each one’s fundamentals can provide a solid foundation for furtherĀ  research into more advanced algorithms and methods.Ā Ā

There is still a lot to cover, includingĀ  quality metrics,Ā  cross-validation,Ā  theĀ  classĀ  disparity inĀ  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 ScienceĀ 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.