Learn how to use Python for data analysis. Join today to learn under the mentorship of an expert data scientist.
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Corporate Served- Python, an open-sourced, high-level language crucial for data scientists, offers exceptional performance in mathematics and statistics. Aspiring data scientists can gain comprehensive insights from our industry-experienced trainers, ensuring proficiency for real-world applications. This dynamic training, including interactive discussions and a 100% placement guarantee, positions you for success in the evolving field of data science. Elevate your skills with us, where Python meets the challenges of real-world data.
- Embark on a learning expedition where Python mastery aligns seamlessly with the demands of the data science landscape, setting you on a path to success. Elevate your skills and navigate the dynamic challenges of the data-driven world with confidence.
Comprehensive Python and mathematics training
Industry-experienced trainers
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Python for Data Science Training in Chennai
- Enroll in our dynamic Python for Data Science in Chennai where our courses are meticulously crafted to align with the latest features, specifications, and industry trends. Explore the world of Python with ample opportunities to learn and grow.
- Master Python for Data Science with us, gaining the expertise needed to thrive in the ever-evolving landscape of data-driven technologies. Enroll today and elevate your skills.
From Scratch to Mastery: Our experienced trainers take you through the fundamentals, starting with expressions and variables. Gain a deep understanding of essential data structures like lists, tuples, dictionaries, and sets.
Beyond Basics: Move beyond the basics as our trainers delve into the utilization and implementation of advanced concepts such as loops, functions, objects, and classes.
Hands-On Data Manipulation: Work seamlessly with data using Pandas and NumPy libraries. Acquire practical skills that bridge the gap between theory and real-world application.
Comprehensive Learning: This training doesn't just provide theoretical knowledge but also offers hands-on practical experience.
Study Materials: As part of our commitment to your learning journey, we provide study materials that complement the course content, ensuring you have the resources you need.
- Furthermore, embarking on our online certification journey will provide you access to exclusive insights about this course. You'll not only gain a foundational understanding but also uncover hidden nuances right from the beginning.
- Possessing this legitimate accreditation opens the door for a sustained commitment to the course. Our trainers are dedicated to assisting you in securing a position in a specific company.
- To be precise, acquiring in-depth knowledge through this certification can potentially lead to annual earnings of around 10.2 lakhs INR. Your investment in learning pays off both in knowledge and tangible career benefits.
- Entering the field of Data Science presents a compelling opportunity due to the high demand for professionals. Choosing this path not only exposes you to the intricacies of the field but also positions you to excel and reach the pinnacle of your career.
- Acquiring a legitimate certification significantly enhances your chances of securing a position within an organization. Moreover, immersing yourself in the specific knowledge of the field propels you toward becoming a proficient professional.
- As a freelancer, you can also achieve substantial financial success. The versatility of your skills opens doors to opportunities in multinational companies. Obtaining this accreditation not only validates your skills but also enables you to work professionally as a recognized Data Scientist. Seize the chance to thrive in this dynamic and lucrative field.
- Opting for this certification offers several compelling reasons. One of the key factors is the substantial demand, promising scope, and widespread applicability across various sectors. If your goal is to become a successful Data Scientist, obtaining this accreditation is undoubtedly a wise choice.
- Your mathematical and statistical skills will undergo refinement, adding depth to your expertise. Furthermore, your programming skills will experience enhancement, making you a proficient Data Scientist. The certification ensures a comprehensive understanding of the intricate details of the field.
- Beyond skill development, this accreditation opens doors to opportunities with major establishments such as TCS, IBM, and more, providing a pathway to a successful and impactful career in Data Science.
- the role of a Data Scientist encompasses a diverse range of responsibilities, with a primary focus on managing substantial volumes of data. Understanding the tasks involved is crucial once you step into the shoes of a Data Scientist.
- Your primary responsibility will involve utilizing raw data to extract valuable insights. Additionally, you'll be tasked with interpreting large datasets and contributing to business-driven decisions through comprehensive data analysis. Automation of data collection and management processes will also fall under your purview.
- In essence, you'll be actively involved in the processing and cleansing of data as part of your job role. The multifaceted nature of a Data Scientist's responsibilities underscores the dynamic and impactful nature of the role.
- Explore opportunities with leading organizations actively seeking Data Scientists, including IBM, TCS, Accenture, Wipro, Amazon, and Cognizant.
- Choosing this specialized field promises multiple benefits. With this accreditation, expect an upward trajectory in your career, securing a highly competitive position and enjoying an augmented salary package. Elevate your professional journey by aligning with the top players in the industry.
- Enrolling in Croma Campus opens doors to a diverse array of certifications spanning various industries, widely recognized within the IT domain.
- Acquiring accreditation from Croma Campus enhances your prospects of securing a position in a reputable workspace. Our interactive training approach not only imparts knowledge but also refines your communication skills.
- Gain access to the Learning Management System, offering a comprehensive overview of your performance, class videos, assignments, and more. Croma Campus ensures a holistic learning experience to empower your professional growth.
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Course Duration
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CURRICULUM & PROJECTS
Python for Data Scientist Training
- Python Training
- Data Analysis and Visualization using Pandas.
- Data Analysis and Visualization using NumPy and MatPlotLib
- Introduction to Data Visualization with Seaborn
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- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Python Keyword and Identiers
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
- 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
- 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
- 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 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
- 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
- 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
- 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)
- 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.)
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- 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
- 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
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- Installing SMTP Python Module
- Sending Email
- Reading from le and sending emails to all users
- 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
- 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
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- Categorical Data
- Numerical Data
- Mean
- Median
- Mode
- Outliers
- Range
- Interquartile range
- Correlation
- Standard Deviation
- Variance
- Box plot
- 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)
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- 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
- 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.
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- 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
- 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
- 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
- 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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FAQ's
Gain a competitive edge with industry-focused Python for Data Science courses at Croma Campus, ensuring practical skills and real-world applications.
Basic knowledge of programming and a strong desire to delve into the world of data science are recommended prerequisites for the Python for Data Science course.
Anyone with an interest in data science, including students, professionals, and enthusiasts, can register for the Python for Data Scientist course.
Experienced and industry-qualified instructors at Croma Campus will guide you through the Python for Data Scientist Course.
The course duration varies, but typically, it is designed to be completed within a reasonable timeframe, considering both learning depth and efficiency.
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Data Science
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