{\rtf1\ansi\ansicpg1252\deff0\nouicompat\deflang2057{\fonttbl{\f0\fnil\fcharset0 Calibri;}} {\*\generator Riched20 6.3.9600}\viewkind4\uc1 \pard\sa200\sl276\slmult1\f0\fs22\lang9 Global Site Tag (gtag.js)\par This is the Global Site Tag (gtag.js) tracking code for this property. Copy and paste this code as the first item into the of every web page that you want to track. If you already have a Global Site Tag on your page, simply add the config line from the snippet below to your existing Global Site Tag.\par \par \b\fs32 Add Below Code\par \b0\fs22\par

data science with python

Data science is future in many domains, now many of organizations are shifted to python software; Python is very powerful tool & easy to learn. Data science course can do beginner or professional , we will cover from scratch , To learn this there are no prerequisites, but if you have knowledge about statistics then it will be very useful for you to understand statistic, if not then we are going to cover all topic of statistic it as well

In this course, you will learn Python programming for handling the large data , applied the statistic , Advance statistic , interpretation of data , Machine Learning algorithms , this course will help you to start your career with as a Data Scientist , which is highly demanded in domestic & International market . After this course if you want to explore your career for Advance level then go for Artificial Intelligence, Neural Network, and Deep Learning etc


Course Outline:

Introduction to Data Science and Analytics
  • • What is Data Science?.
  • • What's the need & why it's in demand ?.
  • • What is Data Analytics ?.
  • • Components of Data Science.
  • • Real-life examples & applications.
  • • Introduction to different programming languages used for Data Science.
Python Programming 1: Basics
  • • Why Python for Data Science?.
  • • Installing Python.
  • • Python IDEs.
  • • Python Basic Syntax & Data types.
  • • Lists.
  • • String Manipulation.
  • • Conditional Statements.
  • • Looping Statements.
  • • Dictionaries.
  • • Tuples.
  • • Functions.
  • • Array.
Python Programming 2: Libraries
  • • Pandas.
  • • Numpy.
  • • Sci-kit Learn.
  • • Matplot library.
  • • Seaborn.
Data Pre-processing & Exploration
  • • Extracting data from different sources.
  • • Reading XLSX, CSV etc. files.
  • • Handling Missing Values.
  • • Handling Outliers.
  • • Different Data Munging Techniques.
  • • One Hot Encoder & Feature Scaling.
Statistics
  • • Mean Mode, Median.
  • • Random Variable.
  • • Probability, Probability Distribution of Random Variables.
  • • Type of Random Variables - Based on Scale of Measurement.
  • • Normal, Binomial, Poisson Distribution.
  • • Standard Normal Distribution and Z-Score.
  • • Sampling & Sampling Distribution.
  • • Central Limit Theorem.
  • • Simulation.
  • • Hypothesis and hypothesis Testing.
  • • Hypothesis Testing using z-test, t-test.
Machine Learning
  • • What is Machine Learning ?.
  • • Overview & Terminologies.
  • • Difference between AI & ML.
  • • Supervised & Unsupervised ML
  • • Supervised ML Models.
  • • Linear & Logistic Regression.
  • • Regression methods, Classification.
  • • Sampling & Sampling Distribution.
  • • K Nearest Neighbours KNN.
  • • Decision Tree, Random Forest.
  • • Unsupervised ML Models.
  • • K Means Clustering.
  • • Under fitting and Overfitting.
  • • Confusion Metrix.
  • • K-Fold Cross Validation.
  • • Regression Evaluation Metrics.
  • • Time Series Analysis.
  • • Support Vector Machine (SVM).
  • • Na'ive Bayes.

Courses

SAS

ADVANCE SAS

CLINICAL SAS

CDISC

Base, Advance Excel

DATA SCIENCE

Tableau

Copyright © 2020 Softnet World | All rights reserved.