According to an IBM report, Data Science jobs would likely grow by 30 percent. The estimated figure of job listing is 2,720,000 for Data Science in 2020

And according to the US Bureau of Labor Statistics, about 11 million jobs will be created by 2026

 

Data Science, Machine Learning and Artificial Intelligence are hottest and trending technologies across the globe, almost every multinational organization is working on it and they need a huge number people who can work on these technologies

 

By keeping all the industry requirements in mind we have designed this course, with this single course you can start your journey in the field of Data Science

 

In this course we tried to cover almost everything that is comes under the umbrella of Data Science,

 

Topics covered:

1) Machine Learning Overview: Types of Machine Learning System, Machine Learning vs Traditional system of Computing, Different Machine Learning Algorithm, Machine Learning Workflow

2) Statistics Basic: Data, Levels of Measurement, Measures of Central Tendency, Population vs Sample, Probability based Sampling methods, Non Probability based Sampling method, Measures of Dispersion, Quartiles and IQR

3) Probability: Introduction to Probability, Permutations, Combinations, Intersection, Union and Complement, Independent and Dependent Events, Conditional Probability, Addition and Multiplication Rules, Bayes’ Theorem

4) Data Pre-Processing: Importing Libraries, Importing Dataset, Working with missing data, Encoding categorical data, Splitting dataset into train and test set, Feature scaling

5) Regression Analysis: Simple Linear Regression, Multiple Linear Regression, Support Vector Regression, Decision Tree, Random Forest Regression

6) Classification Techniques: Logistic Regression, KNN, Support Vector Machine, Decision Tree, Random Forest Classification

7) Natural Language Processing: Tokenization, Stemming, Lemmatization, Stop Words, Vocabulary and Matching, Parts of Speech Tagging, Named Entity Recognition, Sentence Segmentation

8) Artificial Neural Networks (ANNs): The Neuron, Activation Function, Cost Function, Gradient Descent and Back-Propagation, Building the Artificial Neural Networks, Binary Classification with Artificial Neural Networks

9) Convolutional Neural Networks (CNNs): Theory behind Convolutional Neural Networks, Different layers in Convolutional Neural Networks, Building Convolutional Neural Networks, Credit Card Fraud Detection with CNN

10) Recurrent Neural Network (RNNs): Theory behind Recurrent Neural Networks, Vanishing Gradient Problem, Working of LSTM and GRU, IMDB Review Classification with RNN – LSTM

11) Data Analysis with Numpy: NumPy Arrays, Indexing and Selection, NumPy Operations

12) Data Analysis with Pandas: Pandas Series, DataFrames, Multi-index and index hierarchy, Working with Missing Data, Groupby Function, Merging Joining and Concatenating DataFrames, Pandas Operations, Reading and Writing Files

13) Data Visualization with Matplotlib: Functional Method, Object Oriented Method, Subplots Method, Figure size, Aspect ratio and DPI, Matplotlib properties, Different type of plots like Scatter Plot, Bar plot, Histogram, Pie Chart

14) Python Crash Course: Part 1: Data Types,  Part 2: Python Statements, Part 3: Functions, Part 4: Object Oriented Programming

 

Learn Data Science to advance your Career and Increase your knowledge in a fun and practical way !

 

Regards,

Vijay Gadhave