Data Analysis with Python|python data science certification

Course Overview

Introduction– In the data Analysis with Python Certification, you will learn the basics of data analysis with Python. By the end of this certification, you will learn to browse data from sources like CSVs and SQL, and the way to use libraries like Numpy, Pandas, Matplotlib, and Seaborn to method and visualize data.

About python data science certification– Our data Science with Python Certification Course can establish your mastery of data science and analytics techniques use in Python. for this course, you’ll learn the essential ideas of Python programming and gain in-depth, valuable information in data analytics, machine learning, visualization, visual image, net scraping, and the linguistic communication process. As we’ve seen, Python is an increasingly needed talent for several data science positions, so enhance your career with this interactive, active course. Whether you choose the online Flexi-Pass or training coaching Solutions, you will gain access to 44 hours of instructor-led training delivered through a dozen lessons on your flexible timings. –

Key features

Importing Datasets
Cleaning the Data
Data frame manipulation
Summarizing the Data
Building machine learning Regression models
Building data pipelines

Pre-requisites

For this whole analysis, I'll be employing a Jupyter Notebook. you'll be able to use any Python IDE you wish.
You will need to install libraries along the way, and I will provide links that will walk you through the installation process.
Some Python experience is expected
Python for Data Science

Course Syllabus

DATA ANALYSIS WITH PYTHON Course Syllabus

INTRODUCTION TO DATA ANALYTICS
TYPES OF DATA ANALYTICS
INTRODUCTION TO PYTHON AND BASICS
DATATYPES(LIST,TUPLE,SETS,DICTIONARY)
FLOW CONTROLS(DECISIO N MAKING STATEMENTS,LOOPING STATEMENTS)
USER DEFNED FUNCTIONS,DECORATORS
FILE HANDLING PYTHON,MODULES
PYTHON LIBRARIES FOR PYTHON
NUMPY

Array creations, conversions, dimensional understandings, shaping, reshaping, generating sample large datasets, Linear algebra functionalities and numerical operations .

10. SCIPY LINEAR ALGEBRA OPERATIONS INTERPOLATION NUMERICAL OPERATIONS FAST FOURIER TRANSFORM
11. PANDAS:

Introduction

Pandas DataFrame basics
Understanding data, looking at columns, rows and cells
Subsetting Columns, Rows with methods
Grouped and Aggregated Calculations
Frequency Means and Counts
Basic plot
Pandas Data Structures
Creating your own data (Series and DataFrame)
Series (also called as Vector) Object operations
Broadcasting and Scalar operations
Data Frame Broadcasting (Vectorize)
Making changes to Series and DataFrame
Adding additional Columns
Adding additional Columns
Exporting and Importing Data

MATPLOT LIB:

Introduction
Matplotlib
Statistical Graphics using matplotlib
Univariate
Bivariate
Multivariate Data
Seaborn Library Plotting methodology
Univariate, Bivariate and Multivariate
Pandas Objects Plotting
Histogram, Density Plot, Scatterplot, Hexbin Plot and Boxplot
Seaborn Themes and Styles.

MACHINE LEARNING
Part A :Pandas and NumPy Functionalities:

Linear Models
Linear and Multiple Regressions using statsmodelsandsklearn
Generalized Linear Models
Logistic and Poisson Regressions using statsmodels and sklearn
Survival Analysis
Model diagnostics
Residuals
Comparing Multiple Models
k-Fold Cross-Validation
Regularization
Clustering
k-Means, Dimension Reduction with PCA (Principal Component Analysis)
Hierarchical Clusterings

Data analysis with Python

Data analysis with Python

Course Overview

Course Syllabus

Specifications

Data analysis with Python

Data analysis with Python

Course Overview

Course Syllabus

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DATA ANALYSIS WITH PYTHON

Specifications