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Scientific Distributions Used In Python For Data Science
NumPy, pandas, scikit-learn, stat models, nltk
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This course does not require a prior quantitative or mathematics background. It starts by introducing basic concepts such as the mean, median mode etc. and eventually covers all aspects of an analytics (or) data science career from analysing and preparing raw data to visualizing your findings. If you’re a programmer or a fresh graduate looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic to Advance techniques used by real-world industry data scientists.
Introduction to Data Science with Python
Python Essentials
Scientific Distributions Used In Python For Data Science
NumPy, pandas, scikit-learn, stat models, nltk
Accessing/Importing And Exporting Data Using Python Modules
Data Manipulation – Cleansing – Munging using python modules
Data Analysis – Visualization Using Python
Introduction to Statistics
Introduction to Predictive Modelling
Data Exploration For Modelling
Data Preparation
Segmentation: Solving Segmentation Problems
Linear Regression: Solving Regression Problems
Logistic Regression : Solving Classification Problems
Time Series Forecasting : Solving Forecasting Problems
Machine Learning : Predictive Modelling
Unsupervised Learning : Segmentation
Supervised Learning :- Decision Trees
Supervised Learning :- Ensemble Learning
Supervised Learning :- Artificial Neural Network – ANN
Supervised Learning :- Support Vector Machines
Supervised Learning :-KNN
Supervised Learning :- Naive Bayes
Text Mining And Analytics
Introduction to Data Science with Python
Python Essentials
Accessing/Importing And Exporting Data Using Python Modules
Data Manipulation – Cleansing – Munging using python modules
Data Analysis – Visualization Using Python
Introduction to Statistics
Introduction to Predictive Modelling
Data Exploration For Modelling
Data Preparation
Segmentation: Solving Segmentation Problems
Linear Regression: Solving Regression Problems
Logistic Regression : Solving Classification Problems
Unsupervised Learning : Segmentation
Supervised Learning :- Decision Trees
Time Series Forecasting : Solving Forecasting Problems
Machine Learning : Predictive Modelling
Supervised Learning :- Ensemble Learning
Supervised Learning :- Artificial Neural Network – ANN
Supervised Learning :- Support Vector Machines
Supervised Learning :-KNN
Supervised Learning :- Naive Bayes
Text Mining And Analytics
+91 8055223360
Course completion certificate and Global Certifications are part of our all Master Program
Course completion certificate and Global Certifications are part of our all Master Program
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