Learning Objectives: Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.

Topics:

Python files I/O Functions
Numbers
Strings and related operations
Tuples and related operations
Lists and related operations
Dictionaries and related operations
Sets and related operations


Hands On/Demo:

Tuple - properties, related operations, compared with a list
List - properties, related operations
Dictionary - properties, related operations
Set - properties, related operations


Skills:

File Operations using Python
Working with data types of Python

Learning Objectives: In this Python Data Science course module, you will learn how to create generic python scripts, how to address errors/exceptions in code, and finally how to extract/filter content using Regex.
Topics:

Functions
Function Parameters
Global Variables
Variable Scope and Returning Values
Lambda Functions
Object-Oriented Concepts
Standard Libraries
Modules Used in Python
The Import Statements
Module Search Path
Package Installation Ways
Errors and Exception Handling
Handling Multiple Exceptions


Hands-On/Demo:

Functions - Syntax, Arguments, Keyword Arguments, Return Values
Lambda - Features, Syntax, Options, Compared with the Functions
Sorting - Sequences, Dictionaries, Limitations of Sorting
Errors and Exceptions - Types of Issues, Remediation
Packages and Module - Modules, Import Options, sys Path


Skills:

Error and Exception management in Python
Working with functions in Python

Learning Objectives: This Module of Data Science with Python course helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.

Topics:

NumPy - arrays
Operations on arrays
Indexing slicing and iterating
Reading and writing arrays on files
Pandas - data structures & index operations
Reading and Writing data from Excel/CSV formats into Pandas
matplotlib library
Grids, axes, plots
Markers, colours, fonts and styling
Types of plots - bar graphs, pie charts, histograms
Contour plots


Hands On/Demo:

NumPy library- Creating NumPy array, operations performed on NumPy array
Pandas library- Creating series and dataframes, Importing and exporting data
Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot


Skills:

Probability Distributions in Python
Python for Data Visualization

Learning Objective: Through this Module of data science training, you will understand in detail about Data Manipulation.

Topics:

Basic Functionalities of a data object
Merging of Data objects
Concatenation of data objects
Types of Joins on data objects
Exploring a Dataset
Analysing a dataset


Hands On/Demo:

Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
GroupBy operations
Aggregation
Concatenation
Merging
Joining

Although there are many Python learning resources available, it is recommended to find one that teaches data science very well. You should choose a platform that will teach you interactively and has a curriculum designed to help you along your data science journey. Dean Institute is one such platform as we offer the best online python course for data science that will take you from beginner to data analyst in Python or data scientist in Python.

Although there are many Python learning resources available, it is recommended to find one that teaches data science very well. You should choose a platform that will teach you interactively and has a curriculum designed to help you along your data science journey. Dean Institute is one such platform as we offer the best online python course for data science that will take you from beginner to data analyst in Python or data scientist in Python.

All the instructors at the dean-institute are practitioners from the Industry with a minimum of 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by the dean institute for providing an awesome learning experience to the participants of Python Data Science Training.

Just give us a CALL OR email at [email protected]

This is an industry where opportunities are plenty, so once you have the education and qualifications, the jobs are waiting for you. To name a few, some of the most common job titles for data scientists include:

Business Intelligence Analyst
Data Mining Engineer
Data Architect
Data Scientist
Senior Data Scientist

Data scientists perform a wide range of tasks throughout their daily workday. They gather the data and analyze it to identify patterns and trends. To simplify data problems, they also create and test new algorithms. These tasks can be performed using Python and other tools.

If you are looking for free resources on Python and Data Science then read our blogs on Data Science tutorials and Data Science Interview Questions.