Data Analytics

What you will Learn

Our Data Analytics course, we delve into programming computers to analyze and interpret data like humans. Our research encompasses various methods, such as natural language processing, neural networks, and machine learning. By understanding data analytics, we aim to develop systems capable of processing, reasoning, and learning from data—tasks typically requiring human intelligence

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Course content

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  1. What is Python?
  2. A Brief history of Python Why Should I Learn Python?
  3. Installing Python How to execute.
  4. Python program Write your first program

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Practice Question

  1. What is Python, and why is it popular in the programming community?
  2. Can you provide a brief history of Python’s development?
  3. What are some reasons why someone should learn Python?
  4. How do you install Python on your computer?
  5. What are the different ways to execute Python code?
  6. How do you write and save a Python program?
  7. Could you walk me through the process of writing my first Python program?
  8. What is a Python script, and how does it differ from a Python program?
  9. How do you run a Python script from the command line?
  10. What are some online resources or tutorials for beginners to learn Python programming?
 
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  1. What is a variable?
  2. A Brief Data From and type

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Practice Question

  1. What is a variable in Python?
  2. How do you declare a variable in Python?
  3. What are the rules for naming variables in Python?
  4. How do you assign a value to a variable in Python?
  5. What are the basic data types supported by Python?
  6. How do you determine the data type of a variable in Python?
  7. Explain the difference between mutable and immutable data types in Python.
  8. How do you convert one data type to another in Python?
  9. What is type casting in Python?
  10. Can you provide examples of each basic data type in Python?
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  1. Variables Numbers String Lists ,Tuples & Dictionary and more. 

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Practice Question

  1. What are the two main categories of data types in Python?
  2. Give examples of immutable data types in Python.
  3. Explain the characteristics of mutable data types with examples.
  4. Differentiate between a list and a tuple in Python.
  5. What is the purpose of a dictionary in Python, and how is it defined?
  6. Describe the key features of sets in Python.
  7. What is the boolean data type used for in Python, and how is it represented?
  8. How do you create a complex number in Python?
  9. Explain the concept of type conversion in Python with examples.
  10. Discuss the differences between shallow copy and deep copy in Python, using examples if possible.
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  1.  String CRUD opration.
  2. String Methods.

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Practice Question

  1. What is a string in Python, and how is it represented?
  2. How do you create a string in Python using single quotes (”) and double quotes (“”)?
  3. What is string concatenation, and how is it performed in Python?
  4. How do you access individual characters in a string in Python?
  5. What is string slicing, and how is it used to extract substrings from a string?
  6. How do you find the length of a string in Python?
  7. What are some common string methods in Python, and how are they used?
  8. How do you convert a string to uppercase or lowercase in Python?
  9. What is string formatting, and how can you use it to insert variables into a string?
  10. How do you split a string into a list of substrings based on a delimiter in Python
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  1. Tuple CRUD opration.
  2. Tuple Methods.

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Practice Question

  1. How do you create a tuple in Python? Provide an example.
  2. How do you access elements in a tuple in Python? Give an example.
  3. Is it possible to update the values of a tuple in Python? Explain.
  4. How do you delete an element from a tuple in Python?
  5. How do you concatenate two tuples in Python? Show with an example.
  6. How do you check if an element exists in a tuple in Python?
  7. How do you find the length of a tuple in Python?
  8. Explain tuple slicing in Python with an example.
  9. Discuss the concept of immutability in Python tuples.
  10. What are the advantages of using tuples over lists in Python?
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  1.  List CRUD opration.
  2. List Methods.

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Practice Question

  1. What is a list in Python?
  2. How do you create an empty list in Python?
  3. Explain how to access elements in a list in Python.
  4. What is slicing in Python lists? Provide an example.
  5. How can you check if a specific element exists in a list in Python?
  6. Explain the difference between the append() and extend() methods in Python lists.
  7. How do you remove an element from a list in Python?
  8. What is the difference between the remove() and pop() methods in Python lists?
  9. Explain list comprehension in Python with an example.
  10. How can you sort a list in Python? Provide different methods
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  1. Dict CRUD opration.
  2. Dict Methods.

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Practice Question

  1. What is a dictionary in Python?
  2. How do you create an empty dictionary in Python?
  3. Explain how to access values in a dictionary in Python.
  4. What is the difference between a dictionary and a list in Python?
  5. How do you add a new key-value pair to an existing dictionary in Python?
  6. Explain the difference between the update() and setdefault() methods in Python dictionaries.
  7. How can you remove a key-value pair from a dictionary in Python?
  8. What is dictionary comprehension in Python? Provide an example.
  9. Explain the usage of the keys(), values(), and items() methods in Python dictionaries.
  10. How do you check if a specific key exists in a dictionary in Python?
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  1. Set CRUD opration.
  2. Set Methods.

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Practice Question

  1. What is a set in Python?
  2. How do you create an empty set in Python?
  3. Explain the difference between a set and a list in Python.
  4. How do you add elements to a set in Python?
  5. Can a set contain duplicate elements? Explain.
  6. What is the purpose of using a set in Python?
  7. How can you perform set operations like union, intersection, and difference in Python?
  8. Explain the difference between the add() and update() methods in Python sets.
  9. How do you remove an element from a set in Python?
  10. What is the difference between a set and a frozenset in Python?
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  1. if…statement
  2. if…else statement
  3. elif…statement .

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Practice Question

  1. What is a conditional statement in Python?
  2. How do you write a basic if statement in Python?
  3. What is the purpose of the else keyword in an if-else statement?
  4. How do you write an if-else statement in Python?
  5. What does the elif keyword do in Python?
  6. How do you nest if statements in Python?
  7. How do you compare two values in Python?
  8. What is the difference between == and = in Python?
  9. How do you check if a number is greater than or equal to another number in Python?
  10. How do you check if a value exists in a list using a conditional statement?
  11. What is the purpose of the in keyword in Python?
  12. How do you use logical operators (and, or, not) in Python conditional statements?
  13. What is a ternary conditional expression in Python?
  14. How do you handle multiple conditions using if-elif-else statements?
  15. What happens if there are no conditions met in a series of if-elif-else statements?
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  1. The while…Loop
  2. The for….Loop .

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Practice Question

  1. What is the purpose of using loops in Python?
  2. How do you create a for loop in Python
  3. How do you create a while loop in Python?
  4. What is the difference between a for loop and a while loop?
  5. How do you iterate through a list using a for loop?
  6. How do you iterate through a string using a for loop?
  7. How do you iterate through a range of numbers using a for loop?
  8. How do you use the range() function in a for loop?
  9. How do you exit a loop prematurely in Python?
  10. How do you skip the current iteration of a loop in Python?
  11. How do you loop through a dictionary using a for loop?
  12. How do you use the enumerate() function in a loop?
  13. How do you loop through multiple lists simultaneously?
  14. How do you find the sum of all elements in a list using a loop?
  15. How do you find the maximum value in a list using a loop?
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  1. Define function Calling a function
  2. Function arguments Built-in functions.

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Practice Question

  1. What is the purpose of using loops in Python?
  2. How do you create a for loop in Python
  3. How do you create a while loop in Python?
  4. What is the difference between a for loop and a while loop?
  5. How do you iterate through a list using a for loop?
  6. How do you iterate through a string using a for loop?
  7. How do you iterate through a range of numbers using a for loop?
  8. How do you use the range() function in a for loop?
  9. How do you exit a loop prematurely in Python?
  10. How do you skip the current iteration of a loop in Python?
  11. How do you loop through a dictionary using a for loop?
  12. How do you use the enumerate() function in a loop?
  13. How do you loop through multiple lists simultaneously?
  14. How do you find the sum of all elements in a list using a loop?
  15. How do you find the maximum value in a list using a loop?
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  1. Introduction about classes & objects
  2. Creating a class & object

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Practice Question

  1. What is a class in Python?
  2. How do you define a class in Python?
  3. What is an object in Python?
  4. How do you create an object from a class in Python?
  5. What is the purpose of the __init__ method in a class?
  6. How do you define attributes in a class?
  7. How do you access attributes of an object in Python?
  8. What is the difference between an instance attribute and a class attribute?
  9. How do you define methods in a class?
  10. What is the purpose of the self parameter in a class method?
  11. How do you call a method on an object in Python?
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  1. What is data analysis? Why python for data analysis?
  2. Essential Python Libraries
  3. Installation and setup Ipython Jupyter Notebook

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Practice Question

  1. What is data analysis, and why is it important in various fields?
  2. Why is Python commonly used for data analysis tasks?
  3. What are some essential Python libraries used for data analysis?
  4. How do you install Python on your computer?
  5. What is the purpose of IPython in data analysis?
  6. How do you install IPython on your computer?
  7. What is Jupyter Notebook, and why is it popular in data analysis?
  8. How do you install Jupyter Notebook on your computer?
  9. What are some advantages of using Jupyter Notebook for data analysis?
  10. How do you create a new Jupyter Notebook and execute Python code within it?
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  1. Creating multidimensional array
  2. NumPy-Data types Array attributes Indexing and Slicing
  3. Creating array views and copies
  4. Manipulating array shapes I/O with NumPy

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Practice Question

  1. What is NumPy, and why is it used in Python for numerical computing?
  2. How do you create a multidimensional array using NumPy?
  3. What are data types in NumPy, and how do you specify them when creating arrays?
  4. How can you access the shape, size, and dimensions of an array in NumPy?
  5. What is indexing, and how do you access specific elements in a NumPy array?
  6. How does slicing work in NumPy arrays, and how can you extract specific portions of an array?
  7. What is the difference between array views and copies in NumPy?
  8. How can you manipulate the shape of a NumPy array, such as reshaping, flattening, or transposing it?
  9. How do you read and write data to files using NumPy for input/output (I/O) operations?
  10. Can you give an example of a real-world application where NumPy would be useful for data manipulation or analysis?
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  1. Installing pandas Pandas Dataframe Pandas Series Data aggregation with
  2. Pandas Dataframe Concatenating and appending Data
  3. Frame Joining Data Frame Handling missing data Data imputation techniques

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Practice Question

  1. How do you install pandas library in Python?
  2. What is a Pandas DataFrame, and how is it different from a Pandas Series?
  3. How do you create a Pandas DataFrame from a dictionary or a list?
  4. What is a Pandas Series, and how is it created?
  5. How do you perform data aggregation with Pandas DataFrame?
  6. What is the difference between concatenating and appending DataFrames in Pandas?
  7. How do you join two DataFrames in Pandas?
  8. What are some common techniques for handling missing data in Pandas?
  9. Can you explain some data imputation techniques used in Pandas to fill missing values?
  10. How would you handle missing data in a real-world dataset using Pandas?
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  1. Writing CSV files with NumPy and pandas
  2. HDF5 format Reading and writing to Excel with pandas
  3. JSON data Parsing HTML with Beautiful Soup PyTables

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Practice Question

  1. How do you write data to a CSV file using NumPy?
  2. How can you save a pandas DataFrame to a CSV file?
  3. What is the HDF5 format, and how do you save data to an HDF5 file using pandas?
  4. How do you read data from an HDF5 file using pandas?
  5. What are some methods for reading and writing Excel files using pandas?
  6. How do you load data from an Excel file into a pandas DataFrame?
  7. What is JSON data, and how do you parse it using Python?
  8. How can you load JSON data into a pandas DataFrame?
  9. What is Beautiful Soup, and how do you use it to parse HTML data in Python?
  10. What is PyTables, and how does it differ from other data storage formats like HDF5
 
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  1. Introduction to SQL for data analysis
  2. Writing SQL queries for data extraction and manipulation

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Practice Question

  1. What is SQL, and what does it stand for?
  2. Why is SQL important for data analysis tasks?
  3. What are the main categories of SQL commands?
  4. How do you write a basic SQL query to select all columns from a table?
  5. What is the purpose of the SELECT statement in SQL?
  6. How do you filter data using the WHERE clause in SQL?
  7. What is the difference between the DISTINCT and ALL keywords in SQL?
  8. How do you use the ORDER BY clause to sort data in SQL?
  9. What are aggregate functions in SQL, and how are they used?
  10. Can you provide an example of a SQL query that involves multiple tables using JOIN operations?
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  1. Basic statistics with NumPy Linear
  2. Algebra with NumPy NumPy random numbers
  3. Creating a NumPy masked array

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Practice Question

  1. What is NumPy, and why is it commonly used for numerical computing in Python?
  2. How do you calculate the mean, median, and standard deviation of an array using NumPy?
  3. What are some basic linear algebra operations you can perform with NumPy?
  4. How do you multiply two matrices using NumPy?
  5. How do you generate random numbers in NumPy?
  6. What is the purpose of the np.random.seed() function in NumPy?
  7. How do you create a masked array in NumPy, and why might you use one?
  8. What is the difference between a masked array and a regular array in NumPy?
  9. How do you perform element-wise operations on a masked array in NumPy?
  10. Can you provide an example of a real-world application where you might use NumPy for basic statistics or linear algebra operations?
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  1. Installation matplotlib Basic matplotlib plots
  2. Scatter plots Saving plots to file plotting functions in pandas
  3. Time Series Analysis Advanced
  4. visualization with seaboard Data visualization with Plotly & Dash

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Practice Question

  1. How do you install the matplotlib library in Python?
  2. What are some basic types of plots you can create using matplotlib?
  3. How do you create a line plot using matplotlib?
  4. What is a scatter plot, and how can you create one using matplotlib?
  5. How do you save a plot created with matplotlib to a file?
  6. How can you plot functions in pandas DataFrame using matplotlib?
  7. What is time series analysis, and why is it important in data analysis?
  8. How can you plot time series data using matplotlib?
  9. What is Seaborn, and how does it differ from matplotlib?
  10. What are some advantages of using Plotly and Dash for data visualization and dashboard creation?
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Description

Our Data Analytics course provides an introduction to key concepts and techniques used in analyzing and interpreting data. Through hands-on exercises and real-world examples, you’ll learn how to use popular tools and libraries such as Python, pandas, NumPy, and matplotlib for data manipulation, visualization, and statistical analysis. Whether you’re new to data analytics or looking to enhance your skills, this course will equip you with the knowledge and practical experience needed to excel in the field of data analytics

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