Python
Data Analytics with Python
Data Analytics with Python 3 months

English, Russian, Azerbaijani

Trainer: Fərid Əmirov

₼900.00

₼950.00
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Upon completion of the course, students are awarded a certificate

Course materials

Why Python?

Introduction to Python.

Anaconda and spyder setup.

Python version differences.

Installing and running iPython.

Print and functions.

Simple values and arithmetic operations.

Variables.

Data types and conversion of data types.

String.

Text operations Operations and methods on strings

Format method and eval, exec etc.

functions.

Condition elements.

if, elif, else.

Periods.

for, while.

Range, pass, break, continue.

Iterators, Iterables.

Generators.

Lists and methods.

Tupple and methods.

Set and methods.

Dictionary and methods.

Enumarate, zip, dir help, etc.

such important methods.

Similarities and differences between list, tuple, set, and dictionary types.

Functions

Return, yield.

map, filter, lambda, reduce.

*args and *kwargs approaches and implementations.

Global scope, local scope, built-in scope.

Difference between method and function.

Error handling.

Try, except, finally blocks.

Time and date operations.

Numpy

Numpy module and methods.

Analysis and description of data using numpy.

Pandas

Pandas module and methods.

Series, DataFrame dtypes, dtype, size, info, any, all, isnull, ndim, axes, values, head, tail, concat, index, keys, items, etc.

Working with DataFrames.

DataFrame methods.

drop, loc, iloc, join, merge, describe, dropna, groupby, aggregate, filter, transform, apply, pivot_table, select_dtypes, etc.

Condition elements for dataframes.

Reading files with different extensions.

Excel, csv, html, etc.

Analysis of real datasets with pandas and numpy.

Visualization with matplotlib, seabron.

Data graphing methods.

Graph types and applications

barplot, histogram, boxplot, Violin, correlation, scatterplot, lmplot, pairplot, heatmap, lineplot, parallelplot.

Network chart, venn diagram, donutplot, spyder chart, cluster mapplot, insetplot, pointplot, jointplot, pieplot.

kdeplot, swarmplot, pairplot, countplot, word cloud.

Visualization with the matplotlib module.

Visualization with Seaborn.

Time series analysis.

Application and implementation of modules on datasets.

Data pre-processing

Finding and analyzing outliers.

Missing data analysis.

Full-empty string visualization with Missingno module.

Methods, terms and applications of deleting and filling empty lines.

Data normalization, standardization and transformation.

Statistics - scipy

Mean, median, mode.

Variance, standard deviation, range.

Distributions- Normal Distribution.

Hypothesis tests-.

Course description

What is Data Analytics?

 

It is a field of science that investigates the acquisition of useful information for a certain purpose from information that has no meaning without processing, and the necessary methods and methods for this. 

Types of Data Analytics:

 

  1. Descriptive analytics examine past events and include statistical information about events: Monthly revenue, quarterly sales, annual web traffic, etc. This type of context can be presented in the form of graphical tables, reports and dashboards.

 

  1. Diagnostic analytics focuses on historical data like descriptive analytics. But this analysis looks for cause and effect to show why something happened. The goal is to identify relationships between past events in order to determine causes. Diagnostic analytics help the company determine the cause of a positive or negative result.

 

  1. Predictive analytics predicts what will happen by detecting trends in descriptive and diagnostic analyses. A predictive analyst takes past data and feeds it to a machine learning model to understand key trends. The model is then applied to the current data to predict what will happen next.

 

 

  1. After predictive analytics, what should we do if we have an idea of ​​what will happen in the future? The prescriptive analyst emerges at this stage and tries to determine what work should be done. This analysis aims to solve potential problems and often requires the use of machine learning algorithms.

 

 

Who is a Data Analyst?

 

A data analyst takes data using various methods and systems, analyzes and analyzes the data, and helps companies make better business decisions by creating visual reports on the results and predictions based on them.

 

 

Data analysts mostly use the Python programming language in their work. Python is a huge language. 

The Data Analyst will master the structures, methods, functions and visualizations used for data analysis with Python and the necessary introductory and intermediate levels.

 

 

What will data analytics training with Python teach you? 

 

Perform addition, subtraction, grouping and rotation operations on data with Python's popular "Pandas" library

Visualize data aesthetically, create reports and beautiful presentations and make them understandable

Extract data from files such as Excel, CSV, add data or create files in the desired extension.

Resolve common problems with damaged or incomplete datasets.

Learn multiple methods and properties with multiple Pandas objects. Having a strong understanding of manipulation of 1D, 2D and 3D datasets.

Gain definitive experience and knowledge by working with real data sets.