ExcelR’s motto is to raise excellence and work on the vision of training to placement. The participants of data science course in Hyderabad get assured placement in top multinational companies. ExcelR is partnered with University Malaysia, Sarawak (UNIMAS). UNIMAS was founded in 1992. It is ranked among top 200th institutes in Asian university ranking. At present, UNIMAS consists of 3 institutes and 60 centers. Acquiring a certificate from UNIMAS is a tough goal. ExcelR’s expert training faculty trains the participants with accurate assignments and projects to earn the certificate from UNIMAS.
ExcelR is also partnered with Tata Consultancy Services (TCS). TCS is an Indian multinational company and operates in 46 countries. It is one of the most valuable IT brands worldwide. Forbes has given TCS a ranking of 64 in the world’s most innovative companies ranking. It is the 2nd largest IT service provider worldwide. Participants can avail a TCS certification with the help of ExcelR. Participants also get an opportunity to interview at TCS and get placed in it.
The data science course in Hyderabad completely trains an individual in the field of data science. It covers all the small and big topics that come under the data science domain in the form of modules. The data science course starts with Python programming language module. Participants will be introduced to the importance of the Python language, its installation, the comparison between different versions, Python libraries, basics of Anaconda, visualizing data, Seaborn library, introduction to Sklearn library, and lot more. There are tons of opportunities open for an individual upon the successful completion of the data science course.
The data science course in Hyderabad also covers the topic of statistical analysis, including data science, management methodology, statistics, data analysis, cleansing, preparation, feature engineering, understanding business decision, graphical techniques in analytics, R programming, and more. The hypothesis testing module covers the complete understanding of null hypothesis, alternative hypothesis, and types of hypothesis testing, prerequisites of hypothesis testing, interpretation of the results and how it is carried out in Python and R.
The linear regression module covers topics like components of linear regression, multiple linear regression, implementation, and real-life examples. The logistic regression module covers various topics including principles and types of logistic regression, analyzing attribute data, regression statistics, multiple logistic regression, confusion matrix, and more.
There are also small modules, covering topics like advanced regression, multinomial regression, discrete probability distribution, and more. The unsupervised data mining algorithm module covers topics like clustering algorithm, hierarchical clustering, k-means clustering, and real-life examples of it. The dimensionality reduction module covers Principle Component Analysis (PCA) and SVD. The network analytics module covers tools like NodeXL in detail. The association rules module covers the apriori algorithm and the association rules algorithm in detail with examples. The recommender system module covers insights about online recommender systems, content-based filtering, content-based recommender systems, recommendation engine algorithms, people to people collaborative filtering, item to item collaborative filtering, and more.
The machine learning classifiers KNN module of data science course in Hyderabad covers the KNN classifier in detail with real-life examples. The data visualization module covers what exactly data visualization is all about. It includes creating data visualization charts, visualization tools, Tableau, principles of visual design analytics, and more. One complete module is dedicated to the data visualization tool Tableau. It includes topics like Tableau online, server, desktop, preparation, public vs desktop, interactive dashboards, reader, types of data, architecture, working, data source page, data interpretation, discrete data vs continuous data, and user interface of Tableau. Other modules covering more topics about Tableau include charts on Tableau, connecting multiple sheets and data sources to Tableau, Tableau filtration and data sources, interaction and grouping data, time series chart, maps and images in Tableau, analytical techniques, calculation, and advanced charts in Tableau, and Tableau integration with other tools.
The next module is to understand the business model. It trains the participants to understand the business objectives, constraints, creating a business case and project charter, its components, and all that is required to solve a business problem using data science. There are also modules that cover data collection, data cleansing, feature engineering, exploring data analysis, an overview of data mining. A model deployment module trains the participant on how to close a data science or artificial intelligence project. It explains the steps of evaluating project success through various criteria. It covers how to deploy the solution on the client-side. It deals in providing easy viewing of the solution and results to the customer, thus making participants industry-ready.
The data science course in Hyderabad also covers Big Data, Hadoop and its elements. The Big Data module covers topics like components, challenges, tools, and architecture of Big Data and its processing. The Hadoop and its components module cover topics such as components of Hadoop, it’s Master/Slave architecture, demons of storage component, Hadoop distributed file system, processing with MapReduce, resource manager, and Hadoop clusters. There are modules that cover Hadoop elements in detail like Apache Spark, Apache Hive, and Apache SQOOP. The decision tree and random forest module cover in detail both the algorithms along with the important terms included in it. The naive Bayes classifier module covers the algorithm and its business applications in detail. Other important modules of data science course in Hyderabad include natural language processing, text mining, forecasting. It also includes assignments and live projects.