Join the Python with Data Science Course at Croma Campus and master essential Python concepts effortlessly!
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Corporate Served- Our Python Data Science Training is here to help you learn everything you need to know about data analysis and machine learning using Python. This course covers both the basics and more advanced topics. You will learn to work with data using popular tools like Pandas, NumPy, and Matplotlib. You will also get hands-on experience with machine learning algorithms, data cleaning, and data visualization. By the end of the Python with Data Science Course, You will be ready to solve real-world data problems and make data-driven decisions that businesses rely on.
- Prerequisites:
- Who Should Enroll Python with Data Science Course is perfect for anyone interested in data science and machine learning. If you're a student, a beginner, or someone already working in tech or analytics and want to level up, this course is for you. No advanced experience is neededjust a curiosity to learn and explore data!
- Prerequisite Knowledge You dont need any previous knowledge of data science or programming. However, a basic understanding of mathematics and problem-solving skills will help you succeed. Well cover everything from scratch!
Python with Data Science Course
- This course is carefully designed to help you understand the important concepts and techniques in data science. The main goals of the course are:
- Learning Outcomes:
- After completing this course, you will be able to:
Learn Python for Data Science: You will get a strong foundation in Python programming, starting from the basics and moving to more advanced topics like machine learning.
Prepare and Clean Data: Learn how to clean and organize messy data using Pandas and NumPy. This includes fixing missing data, normalizing values, and turning text data into numbers.
Explore Data: Understand data better by visualizing it and finding trends using tools like Matplotlib and Seaborn.
Learn Machine Learning: You will learn how to build machine learning models with Scikit-learn to predict outcomes and classify data.
Work with Advanced Techniques: You will dive into deep learning with tools like TensorFlow and Keras to build neural networks for advanced tasks.
Evaluate Your Models: Learn how to test and improve your machine learning models using cross-validation, accuracy scores, and other techniques.
Data Visualization: Create charts, graphs, and dashboards to visualize data and make your findings easy to understand for others.
Real Projects: You will apply your knowledge by working on real-world projects that simulate the challenges You will face in a data science job.
Build machine learning models that predict future outcomes.
Clean and prepare data for analysis.
Create clear data visualizations to present your findings.
Work with Pythons powerful libraries like Pandas, NumPy, and Matplotlib.
Apply statistical methods to make sense of data.
Develop real-world solutions for data problems.
- After completing Python with Data Science Course, you can expect a competitive salary when starting your career in data science. The salary ranges for freshers are:
- As you gain more experience, your salary could rise to over 1,00,00,000 per year depending on your expertise and job role.
Data Analyst: 50,00,000 - 66,40,000 per year
Junior Data Scientist: 58,10,000 - 74,70,000 per year
Machine Learning Engineer: 66,40,000 - 83,00,000 per year
Data Engineer: 62,25,000 - 78,85,000 per year
- Once you complete the training, you will see plenty of career growth opportunities:
Move to Senior Roles: As you gain experience, you can progress into senior roles like Senior Data Scientist or Machine Learning Engineer.
Specialize in Advanced Areas: You can specialize in specific areas like deep learning or artificial intelligence (AI) for even higher-paying roles.
Start Consulting: With your skills, you could work as a freelance data consultant or even start your own data science consultancy business.
Leadership Positions: As you become an expert, you can eventually lead teams as a Chief Data Scientist or head the data science strategy for a company.
- Python Data Science Training has gained immense popularity due to the following reasons:
Python is Easy and Powerful: Python is simple to learn but extremely powerful for data science tasks. Its easy-to-read code and useful libraries make it an ideal language for beginners and experts alike.
High Demand in Jobs: Data science is one of the fastest-growing fields. Python is widely used by many companies, meaning there are lots of job opportunities for those with Python and data science skills.
Wide Range of Libraries: Python has many libraries that make data science tasks easier, including Pandas for data manipulation, Scikit-learn for machine learning, and TensorFlow for deep learning.
Real-World Use: Many big companies like Google, Facebook, and Amazon use Python for their data science and AI projects, which shows how valuable this skill is in the real world.
- After completing the Python Course for Data Science, You will be ready for different roles, each with specific responsibilities. Some common job roles include:
- Data Scientist:
- Data Analyst:
- Machine Learning Engineer:
- Data Engineer:
- AI Specialist:
Build and train machine learning models.
Clean and prepare data for analysis.
Find insights and present them to stakeholders.
Extract, analyze, and visualize data to help make business decisions.
Use tools like Pandas and Matplotlib to explore data and present it in easy-to-understand charts.
Develop machine learning models and put them into real-time production.
Use libraries like Scikit-learn, TensorFlow, and Keras to build predictive systems.
Build and manage the systems that collect, store, and organize data.
Make sure the data is ready for analysis by data scientists.
- Data science skills are in high demand across various industries, including:
- Industry Use Cases:
- Data science isnt just theoreticalits used to solve real problems in many industries. Some examples include:
Technology: Tech companies hire data scientists to improve products, create new features, and help make data-driven decisions.
Finance: Financial institutions use data science for things like fraud detection, analyzing market trends, and improving investment strategies.
Healthcare: In healthcare, data science helps improve patient care, optimize treatments, and analyze medical data for better results.
Retail & E-Commerce: Retailers use data science to predict demand, personalised recommendations, and optimize supply chains.
Marketing & Advertising: Data science is used to target the right customers, optimize ad campaigns, and track customer behavior.
Finance: Detecting fraud and predicting stock prices.
Healthcare: Predicting disease outbreaks or analyzing patient data.
E-Commerce: Recommending products based on customer behavior.
Social media: Analyzing trends and user sentiments.
- After completing Python Course for Data Science, you will receive a Certificate of Completion. This certificate shows that you have gained essential skills in Python and data science. It will be a valuable addition to your resume, helping you stand out to employers.
- Post-Course Support:
- Even after the course ends, you will have continued access to resources like:
- Tools Covered:
- Projects Covered:
Course materials: You will have lifetime access to recordings, lectures, and practice exercises.
Career Support: Our team helps you with resume building, job applications, and connecting with employers.
Python Libraries: Pandas, NumPy, Matplotlib, Seaborn, and Scikit-learn.
Machine Learning Tools: TensorFlow, Keras, and other tools to build AI models.
Data Handling: SQL, Excel, and data visualization tools for cleaning and presenting data.
Cloud Tools: Learn how to use cloud platforms like AWS for handling big data.
Data Visualization Project: Create various charts and graphs to visualize a dataset.
Machine Learning Project: Build a model to predict future outcomes.
Real-World Data Science Problem: Work on an actual data science problem from a business or research perspective.
- You May Also Read:
Why you should choose Python with Data Science Course?
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Course Duration
32 Hrs.
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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
- 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
- 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)
- 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.
- 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
Mock Interviews
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FAQ's
Well, Croma Campus is the only institution existing that holds thousands of candidates getting placed after acquiring training from here.
This course will hardly take your 3 months to fully get into its detailing.
It's not mandatory though, but it will be appreciable if you will belong from the computer-science background.
The training will be imparted by industry experts holding numerous years of experience in this field.
No, Python Course for Data Science is perfect for beginners. You will start from the basics and move on to more advanced topics.
Most students finish within 2-6 months, depending on whether you choose a self-paced or scheduled course.
Yes, the course includes hands-on projects that use real-world datasets so you can apply your learning.
No, Python and all the tools we use are free and open-source, so you don’t have to purchase anything extra.
There are many roles available such as Data Scientist, Data Analyst, and Machine Learning Engineer, with companies across various industries looking for skilled professionals.
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