Certification from UNIMAS
ExcelR is a proud partner of Universiti Malaysia Saravak (UNIMAS), Malaysia’s 1st public University and ranked 8th top university in Malaysia and ranked among top 200th in Asian University Rankings 2017 by QS World University Rankings. Participants will be awarded Data Science international certification from UNIMAS, after succesfully clearning the online examination. Participants who complete the assignments and projects will get the eligibilty to take the online exam.Thorough preparation is required by the participants to crack the exam. ExcelR’s faculty will do the necessary handholding.Mock papers and practice tests will be provided to the eligible participants which help them to successfully clear the examination.
Data Science Certification Course Training In Abu Dhabi, United Arab Emirates(UAE)
ExcelR offers an interactive instructor-led 160 hours of virtual online Data Science certification course training in abu dhabi, the most comprehensive Data Science course in the market, covering the complete Data Science lifecycle concepts from Data Extraction, Data Cleansing, Data Integration, Data Mining, building Prediction models and Data Visualization. Skills and tools ranging from Statistical Analysis, Text mining, Regression models, Natural Language Processing (NLP), Hypothesis testing, Predictive Analytics, Machine Learning, R Studio, XLminer, Tableau, Minitab, programming languages like R, Python for Data Science are covered extensively as part of this 160 hours of Data Science training. Participants will be provided an opportunity to work on 60+ assignments and one capstone project which ensures hands-on experience for the participants. ExcelR is considered as the leader in the space of Data Science training in Abu Dhabi, United Arab Emirates(UAE).
Data Mining is unveiling the underlying patterns, wonderful insights which go unnoticed otherwise, from the large amounts of data. Data mining tools predict behaviours and future trends, allowing businesses to make proactive, unbiased and scientific-driven decisions. Data mining has powerful tools and techniques that answer business questions in a scientific manner, which traditional methods cannot answer. Adoption of data mining concepts in decision making changed the companies, the way they operate the business and improved revenues significantlyData mining can be broadly categorized into two branches i.e. supervised learning and unsupervised learning. Unsupervised learning deals with identifying significant facts, relationships, hidden patterns, trends and anomalies. Clustering, Principle Component Analysis, Association Rules, etc., are considered unsupervised learning. Supervised learning deals with prediction and classification of the data with machine learning algorithms. Data Mining concepts will be extensively covered as part of our Data Science certification course training.
Python for Data Science
Learn about the installation of Python using various distributions, python operators, string handling, various data types, lists, tuple, string, dictionary, conditional statements, functions, loops, modules, packages. Also learn about file handling, exception handling, regular expressions and data analytics. Learn about exploratory data analysis and data visualization libraries such as Numpy, Pandas, Matplotlib, Seaborn. Data Science concepts are extremely pivotal and hence participants will learn about Linear regression, Logistic regression, Multinomial regression, KNN, Naive Bayes, Decision Tree, Random Forest, Ensemble techniques and black box techniques such as Support Vector Machine and Neural Network. Lastly data optimization techniques such as Gradient Descent, etc.
R for Data Science
As part of the Data Science certification course R programming and R Studio will be extensively covered. Learn about basics of R for Data Science, ranging from reading all the wide variety of files such as sas, spss, minitab, pdf, excel, csv, text, etc. Understanding about various mandatory packages, connecting to odbc. Learn about vectors, lists, matrices, dataframes, inbuild functions, combining datasets using cbind, rbind, merge, etc. Data Manipulation techniques such as grep, grepl, sub, gsub, regexpr, gregexpr, apply, lapply, sapply, tapply, etc., will be explained in finer details. Data Exploration techniques such as summarize, aggregate, Hmisc package, describe function, will be explained using detailed real-world case studies. Learn about Data Visualization techniques plot, line plot, scatter plot, box plot, scatter plot, star plot, stem and leaf plot, pie chart, histogram, rug plot, sunflower plot, hexbin plot, tableplot, mosaic plot, RColorBrewer package, etc.