Big Data Master Class 2023

includes Data Science, Machine Learning-R/Python, Big Data-Hive, Flume,Sqoop, Pig and more.(Beginners To Expert Level)

Ratings 4.60 / 5.00

Ai/Data Scientist - Python/R/Big Data Master Class 2023

What You Will Learn!

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
Advance data manipulation like Merge and Unmerge, cells Text To Column Function, Vlookup, Data Scaling, Consolidation, Conditional Operator If-Else and more.
Analytics For Beginners: Even perform Ai in excel using built-in predictive analytics.
Data Science: like Binomial, Poisson, Hyper Geometric, Negative Binomial, Geometric discrete probability distributions Normal distribution and T-dist
Data Science: Perform hypothesis testing with Normal Distribution and T-distribution using One-Tail and Two-Tail Directional hypothesis.
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.
Perform Anova for multiple levels with and without replication and for count and categorical data using Chi-square Test-Of-Association & Goodness-Of-Fit
Big Data Analytics: The architecture of Hadoop, Map Reduce, YARN, Hadoop Distribution File System, Name node check-pointing, Hadoop Rack Awareness in detail.
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.
Master and perform External Table to share the data among different applications and even partition the table for faster processing.
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: Master how to create supervised models like linear and logistic regression, support vector machine and more to solve real world problems.
Also master to create unsupervised models like k-means and hierarchical clustering, decision trees, random forest to automate solutions for real world problems.
Learn and implement the concepts of Feature Engineering, Principle Component Analysis, Times and more.
NLP: Learn and master data transformation, create text corpus, remove spare terms with Tm package and manipulate text data using regular expression.
Sentiment analysis to negative or the positive response and topic modeling using LDA to identify the topics of 1000 documents without being going through each
Understand the connection of each words using Network analysis or cluster the words used to solve problems like search keywords used to arrive on the website
Bonus: Machine Learning, Deep Learning with Python - Premium Self Learning Resource Pack Free
Full Guide to Linear Regression, Polynomial Regression, Support Vector Regression, Decision Trees Regression, Random Forest Regression and more.
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 Complete Guide to apply ANN for Regression with K-Fold Validation for accuracy.
Reinforcement Learning in 31 Steps. using Upper Confidence Bound(UCB) & Thompson Sampling for Social Media Marketing Campaign Click Through Rate Optimization
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 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
LSTM MultiVariate MultiStep, Auto TS, Thymeboost, NeuralProphet, FbProphet, Synthetic Data Evaluation, OSS, NCR, SMOTE, CouplaGAN, TVAE, A-Z Clustering
Isolation Forest, LOF, Bagging Classifier, Boost Classifier, Auto-TS-Ensemble, Calibrated Classifier, Genetic Algorithms, AutoML, Semi AutoML and more.

Description

The Course is Designed from scratch for Beginners as well as for Experts.

*Updated with Bonus: Machine Learning, Deep Learning with Python - Premium Self Learning Resource Pack Free

Learn the skills of tomorrow, the silicon valley way

Focus on extracting insights from data of any form or shape using a multitude of statistical disciplines for the purpose of creating new products & services or improving the existing ones by predicting the probability in an event. And as the enormity of data is on the rise, there is a desperate need for professionals with data science skills to get valuable insights into it. According to NYTimes there are fewer than 10,000 qualified people in the world and universities are only graduating about 100 candidates each year.

Why data science is so important?

• Twitter Since 2015, the number of posts increased 45% to more than 850,000 tweets per minute.

• YouTube usage has more than tripled in the last two years with users uploading 400 hours of new video each minute of every day.

• Instagram users like 2.5 million posts every minute!

• Google Around 4 million Google searches are conducted worldwide each minute of every day.

• Finally, data sent and received by mobile internet users 1500 000TB.
So, with the above examples of how much data gets generated, now how many hidden insights and patterns for accurate future predictions that we can actually achieve by using data science.

According to Forbes, the annual demand for Data Scientist jobs in the United States itself will increase by 364 million by 2020.
The average salary for a Data Scientist is $170,436.

What is the career progression path for data science professionals?

• Data Scientist: with a vast knowledge of Data Science, Machine Learning, and Business Intelligence tools. Data Scientist stands high as Everest.

• Data Analyst: in 2022, the world will generate data 50times more than now, and with each day that passes by the data generated is infinite with that to analyze those data, data analyst jobs will never have to see the face of recession. On LinkedIn itself, there are average 400 new jobs every 12 hours.

• Data Science Trainer: in this present date a lack of knowledge of these advanced data science techniques gives a vast opportunity to become the fountain of data science for others.

• Business analyst: with the role of defining and managing the business requirements, the business analyst takes the lead in every business decision-making process of the organization.

Who Should Attend!

Anyone looking for a career to machine learning and artificial intelligence.
Anyone looking for a career to Big Data Engineer
Anyone looking for a career to Data Scientist, Business analyst, Data Engineer, Analyst

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