Learn how to use Python with Data Science Course. Join today to learn under the mentorship of an expert data scientist.
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Corporate Served- Python is basically an open-sourced, interpreted, high-level language that is extensively used for object-oriented programming. In fact, it is one of the most-widely languages utilized by data scientists hugely for numerous Data Scientists and their applications respectively. Moreover, Python presents extraordinary performance to deal with mathematics, statistics, and scientific characteristic. If you also dream to turn into a Data Scientist, then you must have detailed information concerning Mathematics, Python, and its implementation precisely.
- Along with training, you will experience a whole new learning experience. In fact, our trainers will indulge you in various kinds of group discussions, and debates to know your understandability of yours concerning this subject.
Here, you will get a probability to gather training from industry-experienced experts conserving numerous years of experience in the applicable fields.
Furthermore, here, our trainers will information you related to its stipulations as well.
Along with training, we will additionally guarantee you 100% placement.
You will get hold of in-depth training as well.
Python for Data Scientist Training
- After enrolling in our courses, you will discover our course content being constructed as per the latest features, specifications, and trends. In a way, you will get ample opportunities to learn new things.
Our trainers will teach you this subject right from the scratch.
They will inform you of the use of "Expressions, and Variables. Describe Data Structures in Python including Lists, Tuples, Dictionaries, Sets, etc.
Further, they will also explain to you the utilization and implementation of Loops, Functions, Objects & Classes. Work with data in Python using Pandas and NumPy libraries respectively.
You will not only receive theoretical information but also get the chance to receive its practical information as well.
You will be provided with study material also.
- Moreover, getting started with our online certification will resource you to accumulate some hidden data involving this course.
In a way, you will in addition reap its stipulations appropriate from the scratch.
With having this legit accreditation in hand, it will be worthwhile to proceed to be regular in this course for a longer time.
Our trainers will in addition aid you to get positioned in a specific company.
To be accurate, by acquiring its in-depth information, you will get a chance to earn around 10.2 lakhs INR per annum respectively.
- Data Scientists are hugely in demand in the market, and by choosing this direction, you will get a fair opportunity to acquire its exposure. This direction will also give you chance to shine and be at the top of your career.
By obtaining this legit certification in hand, your probability of getting settled into an organization will increase.
Furthermore, by way of the potential of imbibing its particular information, you will shortly turn into a professional in this field.
Moreover, as a freelancer also, you will be successful to make an acceptable respectable extent of money.
In fact, your chances of getting settled into a multi-national company will increase.
By obtaining this accreditation, your skills will get validated and you will be able to work professionally as a Data Scientist.
- There are various reasons to choose this certification. One of the significant reasons is its high demand, bright scope, and increased utilization in numerous sectors. If you want to turn into a successful Data Scientist, then you should surely obtain this accreditation.
Your mathematical, and statistical skills will get polished.
Moreover, your programming skills will also get enhanced.
You will turn into a skilled Data Scientist.
You will acquire its minute details.
Moreover, this accreditation will also help you in getting settled with huge establishments like TCS, IBM, etc.
- Well, a Data Scientist is entitled to perform a lot of tasks, one of their main tasks includes handling huge volumes of data. You must know what you might get indulged in after turning into a Data Scientist.
Your foremost duty will be to make use of raw data and extract valuable information from it.
You will also have to interpret large data sets.
Furthermore, your job role will also include giving some business-driven decisions by analysing the data.
In fact, your job role will also be to execute automated data collection and management process.
In a way, you will also have to indulge in processing, and cleansing of data.
- Here are some of the top organizations hiring for Data Scientist:
IBM, TCS, Accenture, Wipro, Amazon, Cognizant, etc, are the top companies hiring skilled Data Scientists.
Opting for this specific field will be beneficial for you in numerous ways.
In fact, by obtaining this accreditation, your career graph will get uplifted.
You will acquire a highly-secure position for yourself.
You will also receive a higher salary package.
- Advantages of getting Certification:
By entering Croma Campus, you will get a wide range of certifications belonging to different industries.
You will find our accreditations being extensively accepted by the IT territory.
Obtaining accreditation from Croma Campus will assist you to get settled into a good workspace.
Moreover, here, you will receive an interactive sort of training, that will also improve your communication skills.
We will give you access to the Learning Management System, where you will be able to see your performance, go through your class videos, assignments, etc.
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Plenary for Python for Data Scientist Training
| Track | Week Days | Weekends | Fast Track |
|---|---|---|---|
| Course Duration | 40-45 Days | 7 Weekends | 8 Days |
| Hours | 1 Hrs. Per Day | 2 Hrs. Per Day | 6+ Hrs. Per Day |
| Training Mode | Classroom/Online | Classroom/Online | Classroom/Online |
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CURRICULUM & PROJECTS
Python for Data Scientist Training
- Python has been a favourite option for Data Scientists who use it for building and using Machine Learning Applications and other Scientific Computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.
- In this program you will learn:
Python Training
Data Analysis and Visualization using Pandas.
Data Analysis and Visualization using NumPy and MatPlotLib
Introduction to Data Visualization with Seaborn
- Introduction To Python
Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.
- Python Keyword and Identiers
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
Python Keyword and Identiers
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators
- Introduction To Variables
Variables, expression condition and function
Global and Local Variables in Python
Packing and Unpacking Arguments
Type Casting in Python
Byte objects vs. string in Python
Variable Scope
- Python Data Type
Declaring and using Numeric data types
Using string data type and string operations
Understanding Non-numeric data types
Understanding the concept of Casting and Boolean.
Strings
List
Tuples
Dictionary
Sets
- Control Structure & Flow
Statements – if, else, elif
How to use nested IF and Else in Python
Loops
Loops and Control Statements.
Jumping Statements – Break, Continue, pass
Looping techniques in Python
How to use Range function in Loop
Programs for printing Patterns in Python
How to use if and else with Loop
Use of Switch Function in Loop
Elegant way of Python Iteration
Generator in Python
How to use nested Loop in Python
Use If and Else in for and While Loop
Examples of Looping with Break and Continue Statement
How to use IN or NOT IN keyword in Python Loop.
- Python Function, Modules and Packages
Python Syntax
Function Call
Return Statement
Arguments in a function – Required, Default, Positional, Variable-length
Write an Empty Function in Python –pass statement.
Lamda/ Anonymous Function
*args and **kwargs
Help function in Python
Scope and Life Time of Variable in Python Function
Nested Loop in Python Function
Recursive Function and Its Advantage and Disadvantage
Organizing python codes using functions
Organizing python projects into modules
Importing own module as well as external modules
Understanding Packages
Random functions in python
Programming using functions, modules & external packages
Map, Filter and Reduce function with Lambda Function
More example of Python Function
- Python Date Time and Calendar
Day, Month, Year, Today, Weekday
IsoWeek day
Date Time
Time, Hour, Minute, Sec, Microsec
Time Delta and UTC
StrfTime, Now
Time stamp and Date Format
Month Calendar
Itermonthdates
Lots of Example on Python Calendar
Create 12-month Calendar
Strftime
Strptime
Format Code list of Data, Time and Cal
Locale’s appropriate date and time
- List
What is List.
List Creation
List Length
List Append
List Insert
List Remove
List Append & Extend using “+” and Keyword
List Delete
List related Keyword in Python
List Revers
List Sorting
List having Multiple Reference
String Split to create a List
List Indexing
List Slicing
List count and Looping
List Comprehension and Nested Comprehension
- Tuple
What is Tuple
Tuple Creation
Accessing Elements in Tuple
Changing a Tuple
Tuple Deletion
Tuple Count
Tuple Index
Tuple Membership
TupleBuilt in Function (Length, Sort)
- Dictionary
Dict Creation
Dict Access (Accessing Dict Values)
Dict Get Method
Dict Add or Modify Elements
Dict Copy
Dict From Keys.
Dict Items
Dict Keys (Updating, Removing and Iterating)
Dict Values
Dict Comprehension
Default Dictionaries
Ordered Dictionaries
Looping Dictionaries
Dict useful methods (Pop, Pop Item, Str , Update etc.)
- Sets
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
Frozen Sets
- Strings
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
- Python Exception Handling
Python Errors and Built-in-Exceptions
Exception handing Try, Except and Finally
Catching Exceptions in Python
Catching Specic Exception in Python
Raising Exception
Try and Finally
- Python File Handling
Opening a File
Python File Modes
Closing File
Writing to a File
Reading from a File
Renaming and Deleting Files in Python
Python Directory and File Management
List Directories and Files
Making New Directory
Changing Directory
- Python Database Interaction
SQL Database connection using
Creating and searching tables
Reading and Storing cong information on database
Programming using database connections
- Contacting user Through Emails Using Python
Installing SMTP Python Module
Sending Email
Reading from le and sending emails to all users
- Reading an excel
Working With Excel
Reading an excel le using Python
Writing to an excel sheet using Python
Python| Reading an excel le
Python | Writing an excel le
Adjusting Rows and Column using Python
ArithmeticOperation in Excel le.
Play with Workbook, Sheets and Cells in Excel using Python
Creating and Removing Sheets
Formatting the Excel File Data
More example of Python Function
- Complete Understanding of OS Module of Python
Check Dirs. (exist or not)
How to split path and extension
How to get user prole detail
Get the path of Desktop, Documents, Downloads etc.
Handle the File System Organization using OS
How to get any les and folder’s details using OS
- Statistics
Categorical Data
Numerical Data
Mean
Median
Mode
Outliers
Range
Interquartile range
Correlation
Standard Deviation
Variance
Box plot
- Pandas
Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
Using the Excel File class to read multiple sheets More Mapping, Filling
Nonvalue’s
Exploring the Data Plotting, Correlations, and Histograms
Getting statistical information about the data Analysis Concepts, Handle the None Values
Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
Applying formulas on the columns Basic Grouping, Concepts of Aggre
gate Function
Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
Under sting the Properties of Pivot Table in Pandas Advanced Reading
CSVs/HTML, Binning, Categorical Data
Exporting the results to Excel Joins:
Python | Pandas Data Frame Inner Join
Under sting the properties of Data Frame Left Join (Left Outer Join)
Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
Pandas | Find Missing Data and Fill and Drop NA Appending DataFrame and Data
Pandas | How to Group Data How to apply Lambda / Function on Data
Frame
Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- NumPy
Introduction to NumPy: Numerical Python
Importing NumPy and Its Properties
NumPy Arrays
Creating an Array from a CSV
Operations an Array from a CSV
Operations with NumPy Arrays
Two-Dimensional Array
Selecting Elements from 1-D Array
Selecting Elements from 2-D Array
Logical Operation with Arrays
Indexing NumPy elements using conditionals
NumPy’s Mean and Axis
NumPy’s Mode, Median and Sum Function
NumPy’s Sort Function and More
- MatPlotLib
Bar Chart using Python MatPlotLib
Column Chart using Python MatPlotLib
Pie Chart using Python MatPlotLib
Area Chart using Python MatPlotLib
Scatter Plot Chart using Python MatPlotLib
Play with Charts Properties Using MatPlotLib
Export the Chart as Image
Understanding plt. subplots () notation
Legend Alignment of Chart using MatPlotLib
Create Charts as Image
Other Useful Properties of Charts.
Complete Understanding of Histograms
Plotting Different Charts, Labels, and Labels Alignment etc.
- Introduction to Seaborn
Introduction to Seaborn
Making a scatter plot with lists
Making a count plot with a list
Using Pandas with seaborn
Tidy vs Untidy data
Making a count plot with a Dataframe
Adding a third variable with hue
Hue and scattera plots
Hue and count plots
- Visualizing Two Quantitative Variables
Introduction to relational plots and subplots
Creating subplots with col and row
Customizing scatters plots
Changing the size of scatter plot points
Changing the style of scatter plot points
Introduction to line plots
Interpreting line plots
Visualizing standard deviation with line plots
Plotting subgroups in line plots
- Visualizing a Categorical and a Quantitative Variable
Current plots and bar plots
Count plots
Bar plot with percentages
Customizing bar plots
Box plots
Create and interpret a box plot
Omitting outliers
Adjusting the whisk
Point plots
Customizing points plots
Point plot with subgroups
- Customizing Seaborn Plots
Changing plot style and colour
Changing style and palette
Changing the scale
Using a custom palette
Adding titles and labels: Part 1
Face Grids vs. Axes Subplots
Adding a title to a face Grid object
Adding title and labels: Part 2
Adding a title and axis labels
Rotating x-tics labels
Putting it all together
Box plot with subgroups
Bar plot with subgroups and subplots
Well done! What’s next
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Projects
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1. In recent times Python learning is hugely in demand. And I was looking for an institution to get myself enrolled in this course to become a Data Scientist. They have the best trainers and an affordable fee structure. It's been month
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