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Complete Data Analytics Course: Become a Python Data Analyst

Name: Complete Data Analytics Course: Become a Python Data Analyst
Rating: 4.2 (18 reviews)

Master Data Science Skills in less than 24 Hrs using Python Programming

Created byDatagai Academy / Ganeshraj Shetty

Last updated 1/2025

English

What you'll learn

Master Complete Data Analysis, Statistical Analysis, Data Wrangling, and Advanced Data Visualization Skills using Python.
Transition today into Data Analyst, Financial Analyst, Business Analyst and Similar in-demand roles.
Enrich your Resume with Skills that will get you hired by top Companies.
Learn numerous Python Modules that would cut down your coding by 40%.
Unlock the power of Automation Skills to become Smart and Efficient at your work.
Gain foundations of Probability and Statistics Essential for an Expert Data Scientist.
Learn Analytical Skills that will make you ready to work in any Domain around the World.
Get an Extra Edge over your peers by learning from a Certified Data Analytics Expert.

Course content

9 sections • 91 lectures • 23h 38m total length

Motivation for learning Python5:56
You will be motivated to Learn Python Programming
Python Introduction8:58
Introduction to Anaconda and Jupyter Note Book7:01
Anaconda Installation9:26
Introduction to Jupyter Notebook Interface8:55

Syntax of a Programming language10:17
Newline Character2:28
Elements_Keywords_Identifiers11:03
Comments_Statement8:19
Variable Assignment13:46
Data Type I in Python Programming9:02
Data Type II31:48
Type Conversion of Data Type8:20
Output Formatting and Input Function13:36
Operators in Python Programming25:35
IF Statements19:56
While loop Statements14:35
Master Python while loops, iterating a code block while the test expression is true, including simple while and while else. Use them to generate multiplication tables and sum natural numbers.
For loop Statement41:33
Break and Continue Statement17:52
Lists-I20:38
Explore Python lists in depth, covering definition, attributes, indexing (zero and negative), mutability, slicing, and operations on lists, nested lists, and list deletion.
Lists-II25:22
Explore Python lists in depth, including concatenation and repetition, membership tests, built-in functions vs list methods, and core operations like sort, append, insert, remove, and copy.
Tuples19:48
Strings25:16
Sets29:52
Explore Python sets and frozen sets, mastering creation, membership, and key methods like add, update, remove, and operations such as union, intersection, difference, and subset checks.
Dictionary21:52
Functions27:23
Function Arguments and parameters14:35
Built-in Functions16:53
Recursive Function9:48
Lambda Function9:29
Modules, Package and Libraries19:04
File IO Operation26:20
Working with Python directory and files10:02
Master working with python directories and files using the OS module, including getcwd, chdir, listdir, rename, mkdir, remove, rmdir, and using shutil for non empty directories.
Exception handling with python15:58
Learn python exception handling, from built-in exceptions to try/except blocks, raise statements, and finally, with examples like zero division and file errors.
Comprehension in Python10:39

Introduction to Matplotlib2:00
Explore how matplotlib enables static, animated, and interactive visualizations in Python, learn to plot line, bar, histogram, scatter, and pie charts with the pyplot module.
Line Chart Plot14:55
In this video, you'll learn how to create Line Chart plots using Matplotlib. Explore the process of plotting data points and connecting them with lines to visualize trends over time or continuous variables. Discover customization options such as adding labels, titles, and adjusting colors and styles to enhance visual clarity. Master techniques for including multiple lines on a single plot and adding markers to highlight specific data points effectively. Gain practical insights into leveraging Matplotlib's features to create informative and visually appealing Line Charts.
Plotting a Bar Chart12:44
In this video, you'll learn how to create Bar Chart plots using Matplotlib. Explore techniques for representing categorical data with bars of varying heights or lengths, ideal for visualizing comparisons between different categories. Discover how to customize bar colors, widths, and orientations to effectively convey information and enhance visual appeal. Master methods for adding labels, titles, and annotations to provide context and clarity to your Bar Charts. Gain practical insights into creating grouped or stacked Bar Charts to compare multiple categories or subsets of data effectively. Strengthen your data visualization skills with Matplotlib's Bar Chart plots through practical demonstrations and examples!
Histogram and Scatter Plot6:07
In this video, you'll explore how to create Histograms and Scatter Plots using Matplotlib. Learn to construct Histograms to visualize the distribution of numerical data, depicting frequency or density across bins, and discover customization options for adjusting bin sizes, colors, and transparency to highlight data distribution patterns effectively. Master techniques for creating Scatter Plots, which visualize relationships between two variables using markers to represent data points, and explore methods for enhancing Scatter Plots with labels, titles, and legends for clear and insightful visual explanations. Enhance your data visualization skills with Matplotlib's Histograms and Scatter Plots through practical demonstrations and examples!
Stack Plot and Pie Plot8:43
In this video, you'll explore how to create Stack Plots and Pie Charts using Matplotlib. Learn to construct Stack Plots to visualize trends over time or cumulative contributions of multiple variables, and discover customization options for adjusting colors and labels to distinguish between different components effectively. Master techniques for creating Pie Charts, which represent parts of a whole using slices to display proportional data, and explore methods for enhancing Pie Charts with labels, percentages, and exploded sections for clearer visual representation. Enhance your data visualization skills with Matplotlib's Stack Plots and Pie Charts through practical demonstrations and examples!
Subplots11:48
In this video, you'll learn how to create subplots using Matplotlib, exploring the concept of organizing multiple plots within a single figure. Discover methods for arranging subplots in grids and customizing their layout to accommodate different visualizations, including techniques for sharing axes and adjusting spacing between subplots. Master the use of subplots to effectively compare data side-by-side or present multiple views of the same dataset. Enhance your data visualization skills with Matplotlib's subplot capabilities through practical demonstrations and examples!

Numpy Introduction15:49
Introduction to Numpy module and its Salient features.
Numpy Array Creation15:51
In this video, you'll explore foundational concepts in working with arrays in NumPy. Learn to create 1-D and 2-D arrays, essential for storing and manipulating structured data, and discover key attributes such as shape, size, and data type that provide essential metadata for array operations. Master techniques for printing array values to gain clarity and insight into array contents, and explore methods for converting arrays into Python lists, with examples showcasing both 1-D and 2-D arrays. Enhance your understanding of array fundamentals and strengthen your proficiency in NumPy operations with practical demonstrations and examples!
Numpy Arange Reshape10:45
In this video, you'll learn essential array manipulation techniques in NumPy. You'll use the arange function to create arrays by generating sequences of numbers with specified intervals and master the skill of reshaping arrays to alter their dimensions while preserving data integrity. Explore methods for constructing 3D arrays, facilitating the representation of complex, multidimensional data structures, and discover techniques for creating 2D arrays with a single column, ideal for organizing and analyzing data in specialized formats. Enhance your proficiency in NumPy array manipulation with practical demonstrations and examples!
Numpy Array Conversion13:28
In this video, you'll learn how to create random arrays of different dimensions and data types in NumPy. You'll generate random 1-D, 2-D, 3-D, and 4-D integer arrays for numerical simulations and computations, as well as explore methods for creating random 1-D and 2-D arrays containing non-integer values, ideal for statistical modeling and data analysis. Discover specialized NumPy functions tailored to create arrays of specific types, providing flexibility based on application needs, and master techniques for converting the data type of arrays, facilitating seamless data integration and manipulation across different formats.
Accessing Array Values22:44
In this video, you'll learn essential techniques for accessing and slicing arrays in NumPy. You'll explore how to effectively retrieve and manipulate values in 1-D arrays by accessing specific elements or subarrays. Additionally, you'll master the skills needed to access and slice values in 2-D arrays, allowing precise extraction of data from rows and columns. Understand the distinction between creating array views, which provide a window into the original array without duplicating data, and array copies, which create independent copies with separate data for independent modifications. Enhance your proficiency in NumPy array manipulation with practical examples and applications!
Numpy_Operations23:00
In this video, you'll discover why NumPy arrays are more efficient than Python's built-in data structures for data manipulation. Learn to compute aggregates, perform element-wise arithmetic, and use specialized NumPy functions for arithmetic, logical, comparison, bitwise, trigonometric, and statistical operations. Master these essential techniques to optimize your data analysis workflows effectively. Join us and enhance your proficiency in NumPy operations with practical examples!
Fancy Indexing and Sorting Arrays15:38
In this video, you'll learn how to selectively extract values from an array based on specified conditions, allowing for precise data filtering. You'll explore the concept of pseudo-random states to ensure reproducibility in random number generation processes. Discover methods for counting and analyzing values within arrays to derive meaningful insights from your data. Additionally, master the techniques of sorting arrays using sort for direct sorting and argsort for obtaining sorted indices. Understand how to sort arrays along rows or columns to effectively organize data structures. Enhance your proficiency in NumPy operations with practical examples and applications!
Array Products and Concatenation23:11
In this video, you'll learn how to calculate the product of arrays using both the matmul and dot functions, and understand the distinct purposes of each. You'll explore how to concatenate arrays of the same dimension seamlessly, and tackle the challenges of concatenating arrays with mixed dimensions. Discover how to flatten arrays for simplified processing, transpose arrays to rearrange data structures, and resize arrays dynamically to fit specific requirements. Mastering these operations will enhance your proficiency in handling complex data tasks using NumPy.
Broadcasting14:50
In this video, you'll learn about Broadcasting in NumPy, which allows efficient operations on arrays with different shapes. Discover simple examples demonstrating how dimensions are aligned and extended for element-wise operations. Understand the rules of Broadcasting and identify scenarios where arrays may be incompatible. Master these fundamental concepts to enhance your ability to manipulate data arrays effectively.

Pandas Introduction4:34
Introduction to Pandas and and its Salient Features.
Pandas Series21:23
In this video, explore the fundamentals of Pandas Series, a powerful data structure for handling one-dimensional data. Learn how to create Series, understand their attributes, and access values efficiently. Discover methods to delete specific values within a Series and techniques to add two Series together. Master these foundational skills to manipulate and analyze data effectively using Pandas Series. Join us and deepen your understanding with practical examples and concise explanations!
Pandas DataFrames24:43
In this video, explore the fundamentals of DataFrames, a powerful data structure in pandas for data manipulation and analysis. Learn how to create new DataFrames, understand their key attributes, and select specific columns for analysis. Discover how to add new columns dynamically, remove rows and columns, and inspect data within a DataFrame for insights. Master techniques to select subsets of rows and columns based on criteria. Gain practical skills to enhance your data exploration and manipulation capabilities efficiently.
Handling missing data25:56
In this video, delve into the nuances of handling missing data effectively in your analysis workflows. Learn about the None datatype and various ways to represent missing values in both arrays and DataFrames. Explore techniques to manage missing data, including dropping rows and columns with NaN values, filling missing values using methods like forward filling and backward filling, both row-wise and column-wise. Gain practical insights into miscellaneous methods for handling missing data scenarios. Master these essential skills to ensure data completeness and reliability in your analyses. Join us and elevate your proficiency in managing missing data with practical examples and effective strategies!
Conditional Selection and Reindexing11:56
In this video, master the art of conditionally selecting values from a DataFrame, exploring both single and multiple conditional selection techniques. Learn to reset and set new indices to organize your data effectively. Gain practical insights into manipulating data based on specific conditions and restructuring your DataFrame for clarity and efficiency in analysis. Join us to enhance your data selection and organization skills with practical examples and concise methods!
Data Input and Data Output11:38
In this video, discover essential techniques for data input and output operations in Python. Learn how to read data from a CSV file, write a DataFrame to a CSV file, and export data to an Excel file seamlessly. Explore methods to read data directly from HTML files and SAS files, gaining practical insights into handling diverse data formats efficiently. Master these fundamental skills to enhance your data manipulation capabilities and streamline your data processing workflows effectively. Join us and deepen your proficiency in data input and output operations with practical examples and clear instructions!
Data Processing22:37
In this video, dive into essential data processing tasks to effectively manage and manipulate data in Python. Learn how to read the first and last rows of a DataFrame for quick insights. Explore techniques to rename column names, delete specific columns, and drop rows and columns simultaneously to streamline data cleaning. Discover how to apply functions to columns for data transformation, and efficiently sort or order your DataFrame by single or multiple columns. Master these foundational techniques to enhance your data processing skills and optimize your data analysis workflows. Join us and elevate your proficiency in data handling with practical examples and concise strategies!
Grouping and Pivot table15:59
In this video, explore the fundamentals of Grouping and Aggregation in data analysis. Learn how to group data by single and multiple columns to organize and summarize information effectively. Discover how Pivot Tables can pivot data to provide insightful summaries across multiple dimensions. Master these techniques to enhance your ability to analyze and derive meaningful insights from complex datasets. Join us and deepen your understanding of Grouping, Aggregation, and Pivot Tables with practical examples and concise explanations!
Concatenating Dataframes and Inserting Rows13:56
In this video, you'll learn how to concatenate DataFrames horizontally and vertically. Explore adding new rows into a DataFrame and replacing or inserting rows at specific indices. Master these essential operations to efficiently merge and modify data structures in Python. Join us to enhance your data manipulation skills with practical examples and concise strategies!
Concatenation and Merging Logic19:09
In this video, understand the logic behind merging and joining two DataFrames, essential for combining data between two datasets. You will learn Horizontal Joining, Vertical Joining, One to One mapping, One to Many Mapping, Many to One mapping, Many to Many mapping, Outer Join of two datasets. These concepts will help you understand joins such as inner, outer, left, and right joins, that will lay a strong foundation towards merging of larger datasets based on common columns or indices.
Merging and Joining15:44
In this video, explore essential techniques for combining data from multiple DataFrames using merging operations. Learn about Inner Join, Left Join, Right Join, and Outer Join to consolidate data based on common keys or indices. Understand how to merge DataFrames with columns having different names, ensuring seamless integration of datasets. Gain practical insights into using the Join function to efficiently merge data based on specified criteria. Master these techniques to enhance your ability to integrate and analyze complex datasets effectively. Join us and elevate your data merging skills with practical examples and clear explanations!
Cartesian Product Between DataFrames5:55
In this video, explore how to compute the Cartesian Product between two DataFrames and understand its role in comprehending joins. Learn how this operation combines every row from the first DataFrame with every row from the second, laying a foundational understanding for various types of joins in relational databases. Gain practical insights into how Cartesian Products illuminate the potential size and structure of join results. Master these concepts to enhance your ability to merge and analyze data effectively. Join us and deepen your understanding of joins with practical examples and clear explanations!
Handling Duplicates in a DataFrames12:31
In this video, you'll explore essential data manipulation techniques for identifying and managing duplicate values in a DataFrame. Learn how to count unique values in a column, detect duplicate rows, and extract or drop duplicates based on specific columns or criteria. Gain practical insights into efficiently cleaning and organizing data to ensure accuracy and reliability in your analyses. Master these techniques to enhance your data handling skills and streamline your data processing workflows.
String Handling23:03
In this video, learn essential techniques for data preprocessing involving string data types. Explore methods to convert columns to string dtype and employ various string functions for effective data processing. Gain practical insights into string manipulation using Regular Expressions/Vectorized String Functions to perform numerous tasks such as Slicing, Concatenation, and Pattern Matching. Master these skills to enhance your ability to handle and analyze textual data efficiently.

Introduction to Statistics6:33
In this video, discover the fundamentals of Statistics, exploring its types and significance in data analysis. Learn about Descriptive Statistics and its attributes. Understand the limitations of Descriptive Statistics in capturing the full complexity of data variability and distribution.
Introduction to inferential Statistics8:48
In this video, explore Inferential Statistics and its attributes, crucial for drawing conclusions and making predictions from data samples. Understand the types of variables and their corresponding statistical methods, including categorical and numerical variables. Gain insights into how inferential statistics help in generalizing findings from samples to populations.
Measures of Central Tendencies13:11
In this video, explore Measures of Central Tendency, including Mean, Median, and Mode, which summarize the typical or central values within data sets. Understand how these measures provide insights into the central tendencies of distributions, using practical examples to illustrate their applications. Learn about Statistical Measures of Position, such as Percentiles and Quartiles, which describe the relative position of data points within a dataset.
Measures of Dispersion10:40
In this video, you'll dive into Measures of Dispersion, crucial for understanding the spread and variability within data sets. Explore different types of Measures of Dispersion, such as Range, Variance, and Standard Deviation, with practical examples illustrating their applications. Learn how to calculate these measures using Python code, gaining insights into data variability and distribution. Master these essential concepts to enhance your ability to analyze and interpret data effectively. Join us and deepen your understanding of Measures of Dispersion with practical examples and Python implementations!
Introduction to Probability9:43
In this video, explore the foundational concept of Probability, essential for understanding uncertainty and risk in data analysis. Learn about different terms in Probability and their practical implications through examples. Discover various types of Random Variables and how they model uncertain outcomes in real-world scenarios. Master these fundamental concepts to enhance your ability to quantify and interpret uncertainty effectively. Join us and deepen your understanding of Probability and Random Variables with practical examples!
Types of Probability functions5:46
In this video, delve into the fundamental concepts of Distribution and Probability Distribution, crucial for statistical analysis. Explore different types of Probability Functions, including their definitions and applications, with practical examples using Python code. Learn how these functions model uncertainty and quantify outcomes in various scenarios. Master these essential concepts to enhance your ability to analyze and interpret data probabilistically. Join us and deepen your understanding of Probability Functions and their practical implementations!
Probability density function17:32
In this video, explore the Probability Density Function (PDF) and its attributes, essential for understanding data distribution. Learn about Normal and Standard Normal Distributions, including the properties of the bell-shaped curve. Discover how to analyze these distributions using Python code, calculate densities, and interpret values within the distribution. Master these foundational concepts to enhance your statistical analysis skills. Join us and deepen your understanding of Probability Density Functions and Normal Distributions!
Cumulative Distribution function16:07
In this video, you'll uncover the Cumulative Distribution Function (CDF) and its significance in statistical analysis. Learn how to explain and compute CDF using Python code, gaining insights into the cumulative probability distribution of your data. Master this fundamental concept to enhance your ability to analyze and interpret data distributions effectively. Join us and deepen your understanding of data analysis with the Cumulative Distribution Function!
Skewness and Kurtosis9:17
In this video, explore Symmetric Distribution, Skewness, and Kurtosis, key concepts in statistical analysis. Learn how to measure and interpret these properties using Python code, gaining insights into the shape and characteristics of data distributions. Master these techniques to enhance your understanding of data symmetry and variability. Join us and deepen your statistical analysis skills with Symmetric Distribution, Skewness, and Kurtosis!
Boxplot17:43
In this video, you'll delve into Box Plots and Violin Plots, understanding their unique characteristics and applications in data visualization. Learn to plot Boxplots and Violin Plots using Python code, calculate Quantiles and Whisker values for robust data analysis, and effectively manage outliers in your datasets. Master these essential techniques to enhance your data interpretation skills. Join us and elevate your data visualization expertise with Box Plots and Violin Plots!
KDE plot17:43
In this video, dive into the essence of Kernels, their properties, and how they enhance data analysis. Explore Kernel Density Estimation (KDE) Plots and their properties, including visualizations that illuminate data distributions. Learn to perform Univariate Analysis using KDE plots and delve into Bivariate Analysis with contour plots. Master these techniques to gain deeper insights into your data. Join us and elevate your analytical skills with KDE visualization!
Covariance15:20
In this video, uncover the fundamentals of Covariance, its attributes, and practical examples. Explore the properties of Covariance values, compare Covariance between two variables, and learn to create a Covariance Matrix. Understand concepts like Negative Covariance and Zero Covariance to effectively analyze relationships in data.
Correlation and Causation21:32
In this video, explore Correlation and its properties, analyze correlations between variables, understand the assumptions necessary before calculating correlation, and visualize correlations using techniques like Heatmaps. Learn about the Coefficient of Determination, grasp the relationship between Correlation and Causation through practical examples.
Introduction to Linear regression18:51
In this video, you'll delve into Regression and its definition, Types of Variables, the practical use of Regression, and the distinctions between Regression and Correlation. Learn Simple Linear Regression, how to calculate the Least Squares Regression Line, understand the Standard Error of Estimate and its assumptions, and implement Linear Regression using Python code. Master these essential techniques to excel in data-driven decision-making.

Exploratory Data Analysis using Classroom DataSet21:42
In this video, you'll discover Univariate, Bivariate, and Multivariate Analysis of classroom datasets. Learn to set the Seaborn style, Create and Analyze Scatter Plots, Kernel Density Estimation Plots, Hex Plots, and Regression Plots.
Exploratory Data Analysis using IMD Rainfall DataSet30:56
In this video, you'll master the Analysis of a rainfall dataset using Heatmaps. Learn to leverage Automation to generate multiple Visualizations, Create and Interpret Heatmaps, and summarize key inferences.
Exploratory Data Analysis of Real Estate Dataset25:45
In this video, you'll explore the Correlation Matrix of a real estate dataset, perform Regression Analysis, and conduct Categorical Analysis of Regression plots. Transform your understanding of real estate data and elevate your analytical skills.
Exploratory Data Analysis using IPL player performance Dataset28:33
In this video, you'll explore different analysis of an IPL player performance dataset using Bar Plots, Count Plots, Strip Plots, Swarm Plots, Boxplots, and Violin Plots. Enhance your ability to visualize and interpret player performance data.

Requirements

There are no prerequisites for taking up this course.
Students from every background without programming knowledge or experience can take this course.
You need to have an Analytical thinking & liking for Math and Numbers.

Description

Welcome to the Comprehensive Advanced Data Analytics Course, your definitive guide to mastering the essential techniques and tools required for cutting-edge data analysis. This course is meticulously designed for data analysts, business intelligence professionals, and those pursuing masters and advanced careers in data science and data analytics. We emphasize practical, real-world applications, moving beyond theory to demonstrate how advanced data analytics can solve complex business problems and uncover valuable insights. This isn't just about numbers; it's about transforming data into strategic decisions. Join us to elevate your analytical skills and thrive in the dynamic world of advanced data analytics.

Introduction to Python Programming for Data Science :

Embark on your journey with an in-depth introduction to Anaconda and Jupyter Notebook—essential platforms throughout the course. Dive into Python programming, the backbone of modern data analytics, data science, and Machine Learning. Our project-based learning approach immediately applies theory to practice, transforming raw data into actionable insights. By mastering these tools, you'll confidently write Python code relevant to both Data Science and Machine Learning, preparing you for complex data analysis tasks and establishing a robust foundation for your career in Machine Learning.

Introduction to Data Visualization using Matplotlib :

Explore data visualization using the Matplotlib module. Simplify complex concepts through clear examples, building a strong foundation in transforming data into impactful insights. Visual representations play a pivotal role in data analytics, data science, and Machine Learning, enhancing your ability to communicate findings effectively. By mastering Matplotlib, you'll elevate your skills to drive decisions based on compelling data-driven insights, distinguishing yourself in the competitive field of Data Science and Machine Learning.

Advanced Numerical Data Structure using Numpy :

Delve into NumPy, the cornerstone of numerical computing in Python, essential for fast, efficient data analysis. Master the manipulation of arrays and matrices, pivotal in handling and analyzing large datasets. Our practical approach ensures quick comprehension and immediate application, empowering you to turn raw data into valuable insights. Proficiency in NumPy is critical for precise data analysis, where speed and accuracy are paramount, positioning you as a top-tier data analyst in both Data Science and Machine Learning.

Advanced Data Manipulation using Pandas :

Master Pandas, a game-changing library for data manipulation and analysis in Python. Navigate dataframes and series with ease, cleaning, filtering, and transforming data for meaningful analysis. Hands-on exercises reveal Pandas practical benefits, enabling you to derive actionable insights effortlessly from complex datasets. Streamline your data analysis workflow with Pandas, enhancing efficiency and accuracy in data analytics—a vital asset in today's competitive landscape of Data Science and Machine Learning.

Handling Dates in Datasets using Pandas :

Dates are crucial in data analytics, data science, and Machine Learning, providing temporal context that enhances insights. Learn to parse, manipulate, and analyze dates effectively with Pandas, transforming raw data into valuable time series. This skill uncovers trends and patterns over time, enriching your data analyses with actionable insights. Mastery of date handling in Pandas distinguishes your data analysis capabilities, essential for success in the fast-paced world of both Data Science and Machine Learning.

Essentials of Probability and Statistics using Scipy :

Probability and statistics underpin precise outcome prediction, risk assessment, and strategic decision-making in data analytics and Machine Learning. Explore uncertainty assessment through probability theory, essential for robust predictions and effective risk management. Statistics empowers analysts to uncover trends and correlations critical for optimizing processes and gaining competitive advantages in data analysis. Each concept is reinforced through practical applications using Python programming, ensuring you can interpret data objectively and drive informed strategies in both Data Science and Machine Learning.

Exploratory Data Analysis (EDA) using Seaborn :

Master Exploratory Data Analysis (EDA) using Seaborn—a versatile Python library. Analyze diverse datasets—from classroom dynamics to IMD rainfall patterns, IPL player performances, and real estate market trends. Seaborn's visualizations reveal critical insights that guide data-driven decisions across education, climate forecasting, sports management, and real estate investments. Proficiency in EDA sharpens analytical skills, extracting actionable insights for competitive advantage in today's dynamic markets of Data Science and Machine Learning.

What sets this course apart from other Data Science courses?

In this course, you will learn Data Analytics, Data Science, and fundamentals essential for Machine Learning in just under 24 hours, meticulously designed by an expert with global credentials and experience in Data Analytics and Machine Learning. Engineered for optimal learning efficiency, the "Advanced Data Analytics Course" offers concise videos and focused content that maximize your understanding. You'll master the essentials of both Data Science and Machine Learning swiftly and effectively, gaining practical skills that are immediately applicable. Whether you're starting from scratch or aiming to enhance your expertise, this course ensures you acquire in-demand skills in the shortest possible time, setting you on a path to excel in the dynamic fields of data analytics, data science, and Machine Learning.

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Who this course is for:

Students who wish to work as a Data Analyst or Similar Roles.
Candidates who wish to add Python for Data Science as a Skill.
Candidates who wish to make a transition into Data Science.
Candidates who wish to unravel the potential of Automation using Python.

Complete Data Analytics Course: Become a Python Data Analyst

What you'll learn

Explore related topics

Course content

Introduction to Python and its Environment5 lectures • 40min

Introduction to Python Programming30 lectures • 8hr 51min

Project : Twitter Data Analysis using Python Programming5 lectures • 48min

Introduction to Data Visualization using Matplotlib6 lectures • 56min

Advanced Numerical Data Structure using Numpy9 lectures • 2hr 35min

Advanced Data Processing, Wrangling and Analysis using Pandas14 lectures • 3hr 49min

Handling Dates in Datasets4 lectures • 1hr 3min

Essentials of Probability and Statistics for Data Analytics14 lectures • 3hr 9min

Project : Exploratory Data Analysis4 lectures • 1hr 47min

Requirements

Description

Who this course is for: