Data Science Course

Data Science is considered as one among the professional field that includes the process of extracting knowledge using the data. There are lots of elements like computer science, mathematics, statistics, and data analysis; all these are meant for developing the models. The ideal models are meant for creating the processes, designing advanced systems and exploring the theories in a better way. Data Science has created a new world of opportunities and make you see the ‘big picture’ of data.

Data science is the extraction of data, This unstructured data is generated from different sources like financial logs, text files, multimedia forms, sensors, and instruments.

Data science certification course provides in-depth knowledge of the systematic process of transforming an organization's data into the statistical form and these data are analyzed with analytic tools. data Analytics is a skill that involves technologies, statistics, data mining to gain business insights from data and statistics.

Learning a data science course from a training center is very simple. But it depends on what you should extract from that course and how much you gained from that course.

 Here we mainly mention about what will one learn after completing the data science Certification course. and some of the topics one will learn in data science certification course are ;

Regression Analysis - Linear & Logistic

Regression is a branch of Statistics. Regression is used in data science to perform predictions for a given dataset. The types of regression include Linear, Polynomial, and Logistic.

Linear regression is a technique in statistics to predict a response variable by fitting a line which might best represent the connection between the dependent and independent variable. Assume you are given a data set that illustrates the sales of ice cream, based on the average temperature on a given day x, across a certain time period. The method of regression learns weights, to fit the training data the best; this can then be used to predict.

Logistic regression -It’s another technique statistics, that is used where the response variable is categorical. The idea of Logistic Regression is to find a relationship between features and the probability of a selected outcome.

Cluster Analysis 

Clustering algorithms are to make sense and extract value from large quantities of structured as well as unstructured data. It permits you to segregate data based on their properties or features and group them into completely different clusters depending on their similarities.

K-means Clustering is the common clustering algorithm in Data Science. As it is very easy to understand and implement, this algorithm forms a critical aspect of introductory data science and machine learning.

In Hierarchical clustering consist of the top-down concept and the bottom-up concept. In Bottom-up concept, treats each data point as an individual cluster at the initial stage. It merges pairs of clusters until you have a one cluster containing all data points. Compare it to a tree wherever the root is the unique cluster that gathers all samples with the leaves because of the clusters with a single sample. 

Data analytics

Big Data analytics is a process of large sets of data are collected, organized and analyzed to discover useful patterns, uncover hidden patterns, market trends, and customers preferences. Data analytics are techniques of data analysis .data analysis techniques include algorithms and data mining methods to give results with fewer calculations.

Data Mining

Data Mining is dealing with the trends in a data set. And using these trends to identify new future patterns. Data mining is an important step in the Knowledge Discovery process. Data mining includes tow techniques supervised learning and unsupervised learning algorithms. Supervised learning is the task of inferring a function from tagged training data. The training data accommodates of a collection of training examples. In supervised learning, each example is a pair consisting of an input object and the desired output value (supervisory signal).

In Data mining, The case of unsupervised learning, It is trying to find a hidden structure in unlabeled data.

Data visualization

Data visualization is the final piece and skill set for accomplished data scientist and data analysts. It involves communicating their findings effectively through graphical suggest that . So that a business analyst or corporate executive, can comprehend the data scientist’s complex findings, and comprehensive presentation is developed.

Time Series Analysis

Statistics could be a series of data points indexed in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. The series Forecasting is the use for  a model to predict future values based on previously observed values.

Private banks are using data mining and analytics to compete successfully and also Credit Card companies use analytics to predict customer risk profile and identify profitable segments. Training for data science at Livewire provide the real-time data tools of R and SAS.

 In today’s competitive world our ambitious youngsters are more career-oriented, for which they are in search of various professional and Job Oriented courses to grow and to match the company requirements. One such in a league is Data Science.

Livewire offering Data Science training program include the real-time data science-related projects which benefit for your future. LIVEWIRE has experienced instructors, who are trained by the product developers and industrial bodies