Affordable Course Fees + Best Education = CIT

Data Science Essentials
Course in Kolkata

One of the Best Computer Training Institute in kolkata for all of you

Low Course Fees | Life Time Support | Real Life Case Studies | 100% Job & Income Opportunity

Book Your Seat Now for Data Science Essentials Course in Kolkata. To Book Call : 9614142256 or Apply Online
Data Science Essentials

Data Science Essentials Course

We Provide Affordable Course Fees for Data Science Essentials Course

Affordable Course Fees + Best Education = CIT

6 Months

Course Duration

10+2 (pass)

Course Eligibility

20+ Topics

Course Module

Online Support

Book, Videos & Lifetime Support

Why Should You Choose Data Science Essentials ?

Dive into the essentials of Data Science with our course, unlocking the power to extract meaningful insights from data. Master fundamental techniques, gain hands-on experience, and propel your career with a skill set highly sought after in today's data-driven world.

Career Readiness: Gain a competitive edge in the job market by acquiring the essential skills demanded by industries leveraging data science, setting a strong foundation for a successful career in this rapidly evolving field.

Practical Skills Development: Engage in hands-on projects and real-world applications, honing your ability to analyze data sets, derive actionable insights, and effectively communicate findings.

Data Science Essentials

Why CIT is the Best Option for your Education ?

Being A Training Institute In Kolkata, We Offered Best Quality Education & Support, So That Everyone Can Grow And Achieve Their Goals Easily.

10+ Years of Experience

Updated, Experienced & Professional Teachers

7 Days Opened with Flexible Class Timing

Live Projects & Workshop Facilities

Lifetime Support & Hand holding

Affordable Course Fees + Best Education = CIT

100% Income Opportunity | 100% Job Guaranteed

!! Book Now !! Data Science Essentials Course

Course Module of Data Science Essentials Course :

Acquire a solid understanding of key data science concepts, methodologies, and tools, laying the groundwork for advanced analytics and problem-solving. (Age Limits : 15 - 35 years)

Acquire a solid understanding of key data science concepts, methodologies, and tools, laying the groundwork for advanced analytics and problem-solving.

Offline Payment Fees System also Available
Data Science Essentials
  • Introduction
    • Hardware Architecture
    • Basic & Type of Programming language
    • Python as a Language
  • Introduction to Python
    • Introduction to programming languages and Python’s role.
    • Installing Python and setting up the development environment.
    • Running Python scripts using the interpreter and IDEs.
    • Basic input and output using print() and input() functions.
    • Variables, data types (integers, floats, strings, booleans), type conversion and basic operations.
  • Data Structures
    • Lists: Creating, indexing, slicing, appending, and modifying etc.
    • Tuples: Creating, indexing, and immutability etc.
    • Dictionaries: Creating, accessing, adding, and modifying key-value pairs etc.
    • Sets: Creating, adding, removing, and set operations etc.
    • Strings: Creating, multiline string,format string,looping through string etc.
  • Control Flow
    • Conditional statements: if, elif, and else.
    • Loops: for loops and while loops.
    • Loop control statements: break and continue.
  • Operators
    • Comparison operators and logical operators.
    • Arithmetic operators,Assignment operators
    • Bitwise operators,Membership operators,Identity operators
  • Functions and Modules
    • Introduction to functions and their importance.
    • Defining functions, parameters, and return values.
    • Scope of variables: global vs local.
    • Built-in functions vs user-defined functions.
    • Introduction to modules and libraries.
    • Importing modules and using their functions.
  • Errors and Exception Handling and Debugging
    • Understanding exceptions and their types.
    • Using try, except, else, and finally blocks.
    • Raising exceptions using raise statement.
    • Debugging techniques: print statements, debugging tools.
    • Syntax error,NameError,ZeroDivisionError,TypeError.
  • Object-Oriented Programming (OOP)
    • Introduction to OOP concepts: classes and objects.
    • Defining classes, attributes, and methods.
    • Inheritance and polymorphism,encapsulation.
    • Method overriding and super() function.
  • File Handling
    • Python File Handling
    • Open a File in Python
    • Reading from a file
    • Writing to file in Python
    • Append to a file in Python
      Closing a file
  • Introduction to Data Analysis and Libraries
    • Introduction to data analysis process
    • Overview of NumPy and Pandas
    • Installing and setting up the libraries
  • Numpy
    • Introduction to Numpy
    • Installing NumPy
    • NumPy arrays: creation, attributes, indexing, slicing
    • Basic mathematical operations with arrays
    • Universal functions (ufuncs)
    • Broadcasting and vectorization
    • Array manipulation: reshaping, stacking, splitting
    • Aggregation and statistics with NumPy
    • Random number generation with NumPy
    • Loading and saving data using NumPy
  • Pandas
    • Installing Pandas
    • Series and DataFrame objects
    • Indexing and selecting data from DataFrames
    • Data cleaning and preprocessing with Pandas
  • Data Manipulation with Pandas
    • Handling missing data
    • Data alignment and merging DataFrames
    • Grouping and aggregation using groupby
    • Applying functions to data with apply
  • Introduction to Data Visualization with Matplotlib
    • Installing Matplotlib
    • Basic plotting: line plots, scatter plots, bar plots
    • Customizing plots: labels, titles, colors
    • Subplots and multiple plots in one figure
  • Intermediate Data Visualization with Matplotlib
    • Plotting with different styles: histograms, pie charts, box plots
    • Adding annotations, legends, and text
    • Saving and exporting plots
    • Creating interactive visualizations with Matplotlib
  • Introduction to Seaborn
    • Installing Seaborn
    • Seaborn’s high-level interface for data visualization
    • Creating aesthetically pleasing statistical plots
    • Exploring relationships between variables with Seaborn
  • Advanced Data Visualization with Seaborn
    • Customizing Seaborn plots: color palettes, themes
    • Visualizing distributions and categorical data
    • Pair plots, heatmaps, and cluster maps with Seaborn
  • Statictics
    • Basic introduction of statictics
  • SQL
    • Basics of SQL
  • Introduction to Machine Learning & Deep Learning
    • Basic introduction of Supervised Learning, Unsupervised Learning
Data Science Essentials

Extra Course Features by CIT:

Course Materials:  Students Will Get Course Documents, Books, E-boks & Online Support.

Course Rating :

Ratings : 5 star

Student Progress:

Skill Developed 85%
Jobs 65%
Freelancing 78%
StartUp 92%

Course Enrollment :

In-Class Timing

Flexibilities :

  • Extra classes in case you miss few classes
  • Doubt Clarence classes
  • Practicals & assessments
  • Job and interview scheduling

Book Your Seat Now !!