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Before you can start learning from this course explained in English (US) you need to have Basic Math Skills, Basic computer skills, Basic Structured Query Language for faster understanding.
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Finally you will learn how to Analytics For Beginners: Learn the interdisciplinary concepts of analytics with the help of success stories, Analytics For Beginners: Become familiar of other companies using analytics as a core part of success stories, Analytics For Beginners: Understand why and how analytics is so important in every profession, Analytics For Beginners: Do data exploration and manipulation like transpose, remove duplicates, pivot table, manipulate data with time & Filter using Excel, Analytics For Beginners: Explore and perform advance data manipulation like Merge and Unmerge, cells Text To Column Function, Vlookup, Data Visualization, Data Scaling, Consolidation, Conditional Operator If-Else and more, Analytics For Beginners: Even perform Ai in excel using built-in predictive analytics, Data Science: Learn the interdisciplinary concepts of data science like Binomial, Poisson, Hyper Geometric, Negative Binomial, Geometric discrete probability distributions also learn and implement Normal distribution and T-distribution of continuous probability distribution, Data Science: Perform hypothesis testing with Normal Distribution and T-distribution using One-Tail and Two-Tail Directional hypothesis even if the sample size is low or the standard deviation is not available or again if the population distribution is not Normal Distribution, Data Science: Chi-square Test-Of-Association, Goodness-Of-Fit and more Follow the program syllabus in our course curriculum to know more in detail, Data Science: Learn how to perform One-Way and Two Way anova for multiple levels or factors influencing the outcome with and without replication and for count and categorical data using Chi-square Test-Of-Association & Goodness-Of-Fit, Big Data Analytics: Learn the architecture of Hadoop, Map Reduce, YARN, Hadoop Distribution File System, Name node check-pointing, Hadoop Rack Awareness in detail, Big Data Analytics: Master and perform big data analysis with on-demand big data tools like PIG, HIVE, Impala and automate to stream live data & workflow with Flume and Scoop, Big Data Analytics: Control parallel processing and create User Define Functions to automate the scripting language without writing a line of code, Big Data Analytics: Master and perform External Table to share the data among different applications and even partition the table for faster processing Follow our program syllabus in course curriculum to know more in detail, R-programming: Learn and master how to manipulate data, impute missing values and visualization using base graphics, ggplot & geo-spatial plots, R-programming: Learn and perform exploratory analysis and work with different file type & data sources, Machine Leaning: Learn to master how to create supervised models like linear and logistic regression, support vector machine and more to solve real world problems, Machine Learning: Also master to create unsupervised models like k-means and hierarchical clustering, decision trees, random forest to automate solutions for real world problems, Machine Learning: Learn and implement the concepts of Feature Engineering, Principle Component Analysis, Times and more Follow our program syllabus in course curriculum to know more in detail, NLP: Learn and master data transformation, create text corpus, remove spare terms with Tm package and manipulate text data using regular expression, NLP: Perform sentiment analysis to know the negative or the positive response and topic modeling using LDA to identify the topics of 1000 documents without being going through each, NLP: Understand the connection of each words using Network analysis or even cluster the words used to solve problems like search keywords used to arrive on the website Follow our program syllabus in course curriculum to know more in detail, Bonus: Machine Learning, Deep Learning with Python - Premium Self Learning Resource Pack Free, 5 Types Regression in 45 lines of code Full Guide to Linear Regression, Polynomial Regression, Support Vector Regression, Decision Trees Regression, Random Forest Regression and more, 7 Types of Classification using python Full guide to knn, logistic, support vector machine, kernel svm, naive bayes, decision tree classification, random forest classification, Let’s Develop Artificial Neural Network in 30 lines of code Simple yet Complete Guide on how to apply ANN for classification, Let’s Develop Artificial Neural Network in 30 lines of code — II Part — II Simple yet Complete Guide on how to apply ANN for Regression with K-Fold Validation for accuracy over accuracy, Reinforcement Learning | In 31 Steps using Upper Confidence Bound(UCB) & Thompson Sampling for Social Media Marketing Campaign Click Through Rate Optimization(using Python & R), What is PCA and How can we apply Real Quick and Easy Way? Learn how to apply Principal Component Analysis (PCA) using python, What is Supervised Linear Discriminant Analysis(LDA) ~ PCA Let’s understand and perform supervised dimensionality reduction, What is Kernel PCA? using R & Python 4 easy line of codes to apply the most advanced PCA for non-linearly separable data, Association Rule Learning using Apriori and Eclat (R Studio) to predict Shopping Behavior, Multi-Layer Perception Time Series Apply State of the Art Deep Learning MLP models for predicting sequence of numbers/time series data, LSTMs for regression Quick and easy guide to solve regression problems with Deep Learnings’ different types of LSTMs, Uni-Variate LSTM Time Series Forecasting Apply State Of The Art Deep Learning Time Series Forecasting with the help of this template, Multi-variate LSTM Time Series Forecasting Apply state of the art deep learning time series forecasting using multiple inputs together to give a powerful prediction, Multi-Step LSTM Time Series Forecasting Apply Advanced Deep Learning Multi-Step Time Series Forecasting with the help of this template, Grid Search For ML & Deep Learning Models Full guide to grid search on finding the best hyper parameters for our regular ml models to deep learning models, 7 types of Multi*-Classification using python Full guide to knn, logistic, support vector machine, kernel svm, naive bayes, decision tree classification, random forest, Deep Learning and even with Grid Search Multi-Classification.