You are currently viewing The importance of statistics in data<br>science

The importance of statistics in data

One of the crucial data science aspects is the process of collecting and interpreting raw data. At the time of extraction of data, professionals have to make predictions and possibilities out of the extracted raw data. All these possibilities are then interpreted with the help of an important and relevant subject in the field of data science known as statistics. The term interpreting data using statistics is also referred to as statistical analysis. As technology is expanding and business functions and decisions are based entirely on data, statistics is said to hold a significant position in the overall procedure of analyzing, managing, presenting, and communicating data.

Statistics is key to managing, controlling, and interpreting data to detect problems that might lead to wrong solutions. Today data is a significant part of almost everyone’s life, and nothing can be possible without it. It is only when data is transformed into the numeric form that data science professionals can discover interminable possibilities and interpret them correctly. Except for statistics, achieving optimum results out of raw data is not possible. Statistics offers different methods and measures to find out valuable inside out of data and information. The following are the points that show why statistics is important in data science.

Also check this data science course in Pune to start a career in Data Science.

 Classification, as well as the organization of data

 Statistical methods are used for classifying data into the visual field. Data classification combined with data organization is important for all companies and business organizations in order to develop strong business plans and make accurate predictions. While some raw data is unusable, some can be very effective and operational. With the help of statistics, it is possible to classify and filter out data that can be processed further to make decisions in the field of data science.

 Data analytics and machine learning

Statistical methods are gateways for grasping the basic concept of machine learning algorithms such as logistic regression. LOOCV and cross-validation are two statistical methods that have brought a great revolution in the field of data analytics and machine learning. These statistical approaches have made it possible for hypothesis testing and reasoning-based research work.

 Detecting anomalies and oddities in data

Today every company and organization is dealing with a massive amount of data daily that they receive from different sources. It is through statistics that the complete process of detecting anomalies in the data structures is possible. Researchers can reject inefficient and incapable data in the early phase itself, thereby saving much of their effort, time as well as resources. Without statistical methods and approaches, the detection of anomalies within data would not have been possible.

Are you looking to become a data scientist? Enroll to best data science courses in Bangalore

Data Visualization

Data visualization refers to the elucidation and depiction of models, perceptions, and structures found in comprehensive, interactive, and effective data formats. All these data formats are easy and simple to process. In order to facilitate data visualization along with data representation, statistics plays a key role. Statistical formats such as pie charts, histograms, and graphs are used for data visualization. Data can be intensified when needed and can be turned into an understandable format through statistical approaches and format.

Helps in reducing assumptions and facilitates mathematical analysis

 The fundamental or basics of mathematical analysis are continuity and differentiability. These two basics are also the fundamental concepts of artificial intelligence, data analytics, and machine learning algorithms. In order to detect the effectiveness of a particular data analytics model or machine learning algorithm, it is important to find out the predictive power. The thumb rule is that fewer the assumptions, the higher the predictive power of the model or algorithm will be. Statistics is the key to reducing the rate of estimation or assumption and bringing out more usability and accuracy in models.

If you are looking for Data Science course, for more details data science course in Hyderabad with placements

 Identifying data structures

 Identifying data structures and detailing networks and values without using statistical distribution methods will not contribute to a reliable and accurate evaluation. Statistics play a significant role in determining the difference in clusters and structures of data, which are heavily dependent on a few variable factors like time, space, etc.

 Logical data interpretation and representation

 A complete series of challenging interactions that take place between variables and factors is known as data. In order to represent the data and display it accurately and logically, different statistical methods are used, such as statistical networks and graphs.

Application of statistical methods for data science and data analytics

 Statistics is necessary for every industrial or business domain for performing data science activities and advanced analytics. Important applications of statistical methods in the field of data science are as follows:

  • Helps to build machine learning algorithms

Statistics is associated with data analytics and data science as it is the basis for different machine learning algorithms such as naïve Bayes and logistic regression, which has constantly evolved and highlighted the importance of statistical methods.

  • Business intelligence

 Statistics is used widely in business and industrial domains for forecasting plans and making predictions waiting for positive outcomes. Statistical terms are associated with data science and data analytics and are considered the first step to understanding data analytics and data science. Experts obtain results for data science using statistics and generate meaningful insights from data which drives more business growth.

  • Understanding big data

 Statistics enable data scientists to determine hidden trends and data patterns and give meaning to them. It is the first step for analyzing data; without it, data scientists will not derive any meaning from the data they analyze. Statistical analysis enables data scientists to identify unseen patterns in data and derive more value for organizations and businesses. Data science also uses statistics to understand big data and customer behavior. For this data, scientists have to use different statistical methods like clustering, latent variable analysis, and dimensionality reduction to understand the buying habits of customers.

Kickstart your career by enrolling in to best institute for data science in Chennai 


Data is a significant part of the present technological world, where corporations and individuals generate huge amounts of data daily that experts must visualize and analyze. Statistics offers the tools and means for finding structures in massive data and provides organizations and individuals and in-depth insight from raw unstructured data. Therefore, statistics is a fundamental step in a data science course that will make accurate predictions. 

Data Science Training Institutes in Other Locations

Tirunelveli, Kothrud, Ahmedabad, Hebbal, Chengalpattu, Borivali, Udaipur, Trichur, Tiruchchirappalli, Srinagar, Ludhiana, Shimoga, Shimla, Siliguri, Rourkela, Roorkee, Pondicherry, Rajkot, Ranchi, Rohtak, Pimpri, Moradabad, Mohali, Meerut, Madurai, Kolhapur, Khammam, Jodhpur, Jamshedpur, Jammu, Jalandhar, Jabalpur, Gandhinagar, Ghaziabad, Gorakhpur, Gwalior, Ernakulam, Erode, Durgapur, Dombivli, Dehradun, Cochin, Bhubaneswar, Bhopal, Anantapur, Anand, Amritsar, Agra , Kharadi, Calicut, Yelahanka, Salem, Thane, Andhra Pradesh, Greater Warangal, Kompally, Mumbai, Anna Nagar, ECIL, Guduvanchery, Kalaburagi, Porur, Chromepet, Kochi, Kolkata, Indore, Navi Mumbai, Raipur, Coimbatore, Bhilai, Dilsukhnagar, Thoraipakkam, Uppal, Vijayawada, Vizag, Gurgaon, Bangalore, Surat, Kanpur, Chennai, Aurangabad, Hoodi,Noida, Trichy, Mangalore, Mysore, Delhi NCR, Chandigarh, Guwahati, Guntur, Varanasi, Faridabad, Thiruvananthapuram, Nashik, Patna, Lucknow, Nagpur, Vadodara, Jaipur, Hyderabad, Pune, Kalyan.

Data Analyst Courses In Other Locations

Tirunelveli, Kothrud, Ahmedabad, Chengalpattu, Borivali, Udaipur, Trichur, Tiruchchirappalli, Srinagar, Ludhiana, Shimoga, Shimla, Siliguri, Rourkela, Roorkee, Pondicherry, Rohtak, Ranchi, Rajkot, Pimpri, Moradabad, Mohali, Meerut, Madurai, Kolhapur, Khammam, Jodhpur, Jamshedpur, Jammu, Jalandhar, Jabalpur, Gwalior, Gorakhpur, Ghaziabad, Gandhinagar, Erode, Ernakulam, Durgapur, Dombivli, Dehradun, Bhubaneswar, Cochin, Bhopal, Anantapur, Anand, Amritsar, Agra, Kharadi, Calicut, Yelahanka, Salem, Thane, Andhra Pradesh, Warangal, Kompally, Mumbai, Anna Nagar, Dilsukhnagar, ECIL, Chromepet, Thoraipakkam, Uppal, Bhilai, Guduvanchery, Indore, Kalaburagi, Kochi, Navi Mumbai, Porur, Raipur, Vijayawada, Vizag, Surat, Kanpur, Aurangabad, Trichy, Mangalore, Mysore, Chandigarh, Guwahati, Guntur, Varanasi, Faridabad, Thiruvananthapuram, Nashik, Patna, Lucknow, Nagpur, Vadodara, Jaipur, Hyderabad, Pune, Kalyan, Delhi, Kolkata, Noida, Chennai, Bangalore, Gurgaon, Coimbatore.

Navigate To:

360DigiTMG – Data Analytics, Data Analyst Course Training in Bangalore

#62/1, Ground Floor, 1st Cross, 2nd Main, Ganganagar 560032, Bangalore, Karnataka

Phone: 1800-212-654321

Get Direction: data science courses in bangalore

Leave a Reply