Name : Abhishek Bisht
Email : Abhishek14.bisht@gmail.com
Course : Data Analyst
Data Analyst Syallbus
Variables & Python Datatypes
Variables in Python serve as named containers for storing data, and data types define the nature of the values they can hold, such as strings, numbers, or boolean values Variables Numbers String Lists, Tuples & Dictionary
Conditional Statement
Conditional statements in Python allow for making decisions in code based on conditions, enabling different paths of execution depending on whether certain expressions evaluate to true or false if statement if else statement elif statement while Loop for Loop
Control statement
Control statements in Python dictate the flow of execution by enabling loops and conditional branching within a program. Continue statement Break statement Pass statement
Functions
Functions in Python are modular blocks of code that perform specific tasks, enhancing code reusability and organization by encapsulating logic into callable units. Define function Calling a function Function arguments Built-in functions Lambda Functions Lambda with Map, Reduce & Filter
Class & Object
Classes in Python define blueprints for creating objects, allowing you to bundle data and behavior into a single entity for organized and efficient programming Introduction about classes & objects Creating a class & object Inheritance Methods Overriding Data hiding
File Handling
File handling in Python provides mechanisms to read, write, and manipulate files, enabling data storage and retrieval for various applications. Create files Read files Write files Delete files
Getting Started with Python Libraries
Getting started with Python libraries involves leveraging pre-built code modules to extend functionality, streamline development, and access a wide range of tools and resources What is data analysis? Why python for data analysis? Essential Python Libraries Installation and setup Ipython Jupyter Notebook
Numpy arrays
NumPy arrays in Python offer efficient data structures for handling large datasets and performing mathematical operations, enhancing numerical computing capabilities Creating multidimensional array NumPy-Data types Array attributes Indexing and Slicing Creating array views and copies Manipulating array shapes I/O with NumPy
Working with pandas
Working with Pandas involves utilizing a versatile data analysis library in Python for tasks such as data cleaning, exploration, transformation, and visualization. Installing pandas, Pandas Dataframe,Pandas Series,Data aggregation with Pandas Dataframe,Concatenating and appending Data Frame,Joining Data Frame,Handling missing data, Data imputation techniques
Data Loading, Storage and File Format​
Data loading, storage, and file formats encompass techniques in Python for reading, writing, and managing various data sources, facilitating seamless data integration and analysis.Writing CSV files with NumPy and pandas,HDF5 format,Reading and writing to Excel with pandas, JSON data, Parsing HTML with Beautiful Soup, PyTables,
Data Analysis with SQL
Data analysis with SQL involves querying and manipulating structured data using SQL (Structured Query Language) in Python, enabling powerful insights and pattern discovery Introduction to SQL for data analysis, Writing SQL queries for data extraction and manipulation
Statistics and Linear Algebra
Statistics and linear algebra in Python provide essential mathematical tools for data analysis, modeling, and machine learning, enhancing decision-making and predictive capabilities Basic statistics with NumPy, Linear Algebra with NumPy, NumPy random numbers, Creating a NumPy masked array
Data Visualization
Data visualization in Python encompasses creating meaningful and informative graphical representations of data, aiding in the understanding and communication of insights Installation matplotlib, Basic matplotlib plots, Scatter plots, Saving plots to file, plotting functions in pandas, Time Series Analysis, Advanced visualization with seaboard, Data visualization with Plotly & Dash