Top 6 Data Analytics Courses with Python With Live Training
Any business or enterprise needs data to function. To gather data helpful for business decision-making, it is vital to gather, process, and analyze data flow in a fast and accurate way. The data science industry is growing rapidly. Information management can be challenging and time-consuming because of the huge data volumes. Python is a widely utilized programming language-cum-software application in scientific computing due to its abundance of data-focused features packages that help accelerate and streamline data processing, hence saving time. Coming to the main point – this article aims to inform you about the institutes that offer data analytics courses with Python.
Top Six Data Analytics Courses with Python
1. Coursera
Coursera offers data analytics courses with Python. During the course, you will be taught the foundation of data analytics using Python to build and assess data models.
The Topics Covered in the Advanced Data Analytics Courses with Python Are as Follows:
- Importing Plus Acquiring Data
- Clean, Prepare, and Format Data
- Manipulate Data Frame
- Summarize Data
- Build Machine Learning Regression Models
- Refine Models
- Create Data Pipelines
What Will You Learn?
You will understand how to conduct exploratory data analysis (EDA), clean and manage data, import data from various sources, and create useful data visualizations. The next step is to develop linear, polynomial, and multiple regression pipelines and models and knows how to assess them to forecast future trends from data. You will study and practice utilizing practical laboratories and projects in addition to video lectures. For loading, manipulating, analyzing, and visualizing fascinating datasets, you will use a variety of open-source source Python libraries, such as Numpy and Pandas. For building machine learning models and making predictions, you will also use sci-kit-learn and scipy.
To clean and prepare data for analysis such as managing missing values, normalization, formatting, and binning of data, you will be developing Python code.
You will be carrying out an exploratory data analysis (EDA) and applying analytical processes to practical datasets with the help of libraries like Scipy, Pandas, and, Numpy.
You will be controlling data with the help of data frames, encapsulating data, comprehending data allocation, carrying out correlation, and creating a data pipeline.
You will be constructing and examining regression models with the help of the machine learning sci-kit-learn library and utilizing them for forecasting and decision-making.
Course Modules
Importing Datasets
You will learn to comprehend data in this module, as well as how to use Python’s libraries to import data from various sources. Then, you will discover how to carry out a few fundamental tasks so that you can begin to discover and investigate the imported data set.
Data Wrangling
You will study how to carry out a few basic data-wrangling tasks that collectively make up the pre-processing stage of data analysis in this module. These jobs include dealing with missing values in data, formatting data then standardizing and making it consistent data, normalizing data, classifying data values into bins, and transforming unequivocal variables into numerical quantifiable variables.
Exploratory Data Analysis
You will learn what exploratory data analysis entails in this module, as well as how to calculate foundational descriptive statistical information, such as median, quartile values, mean, and mode on the data and utilize that information to more fully comprehend the allocation of the data. You will learn how to organize your data for easier visualization, how to differentiate the two consecutive numerical variables using the Pearson correlation technique, how to apply the Chi-square examination to determine whether two unambiguous variables are related and how to interpret the results.
Development of Model
The method to define the explanatory and response variables, as well as the distinctions between the multiple linear regression and simple linear regression models, are taught in this module. You will learn polynomial pipelines and regression, as well as how to examine a model utilizing visualization. Additionally, you will learn how to explain and apply the mean square and R-squared error measures to carry out in-sample tests and quantitatively assess your model. Finally, you will gain knowledge of prediction and decision-making while assessing whether your model is right.
Model Assessment
The significance of model assessment and several methods for data model refinement are covered in this module. You’ll discover how to identify under-fitting and over-fitting inside a predictive model, as well as model selection. Additionally, you will discover how to utilize Grid Search to fine-tune an estimator’s hyper-parameters and how to employ Ridge Regression to normalize and decrease normal errors to avoid over-fitting of a regression model.
Final Project
You will finish the last project in this module, which will be evaluated by your colleagues. You will perform the role of a data analyst who works for a real estate investment trust company and hopes to begin investing in domestic real estate in this last assignment. Your task will be to analyze and forecast the market price of homes using a dataset that contains comprehensive information about home prices based on a variety of property characteristics.
2. Udemy
Udemy is also one of the top online education platforms that provide online courses. Like Coursera, Udemy’s advanced Data Analytics program is also considered one of the best data analytics courses with Python.
Course Name
Learning Python for Data Visualization and Data Analysis
Learn how to analyze, present, and visualize data with the help of Python. The course consists of a ton of example code and thousand-plus hours of video content and materials.
Here is What You Will Learn in This Advanced Data Analytics Course with Python
- You will gain an intermedial skill level for programming Python
- You will learn how to employ the Jupyter notebook domain
- You will know how to utilize the numpy library to build and control arrays
- You will understand how to apply panda’s module with the help of Python to create and organize data.
- Learn how to use JSON, Microsoft Excel Worksheets, and HTML as well as other data formats in Python
- Build data visualization with the help of matplotlib and create the seaborn syllabuses using Python.
- Get portfolios of varied projects including data analysis
The resources you need to master Python and utilize it to visualize and analyze data will be provided through this course. You’ll gain a thorough understanding of Python programming and how to utilize it to analyze data in tandem with scientific computing libraries and modules. Additionally, you will have lifelong access to more than 100 instance Python code notebooks, upcoming additions of different data analysis projects, and, new or latest/updated videos that you can use to build a portfolio to showcase to potential employers.
As You Finish the Advanced Data Analytics Course with Python You Will Do the Following:
- You will gain comprehensive knowledge to do Python Programming
- You will understand how to use Python and Numpy to create and control arrays
- You will understand how to create and examine data sets using pandas
- You will learn how to create stunning data visualizations using the seaborn library and matplotlib
- You will own an outstanding portfolio of examples of Python data analysis projects
- You will know about Machine Learning and be familiar with Sci-Kit Learn
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3. Cognitive Class.ai
Cognitive Class.ai is one of the best places to study data analytics. This institute provides one of the best advanced data analytics courses with Python. This course will teach you about data acquisition – how to acquire foundational insights from dataset data. In this advanced data analytics course with Python, you will start from the fundamentals of Python to discovering various forms of data. You will know the data analysis preparation process, carry out simple statistical examinations, create useful data visualizations, forecast upcoming trends from data, and many more.
During the Advanced Data Analytics Course with Python, You Will Get to Know the Following:
- Importing data sets
- Cleaning and preparing data for analysis
- Controlling Panda data frame
- Summing up data
- Building machine learning models with the help of scikit-learn
- Building data pipelines
The delivery of the Data Analysis with Python course consists of lectures, practical laboratories, and projects. It has the following components:
Data Analysis libraries: You will learn to use Pandas Data frames, multi-dimensional arrays, Sci-Py libraries, and, Numpy, to work with various datasets. You will learn about the open-source library, pandas, which will be then used to load, control, inspect, and visualize interesting datasets. Following that, you will be introduced to a different open-source library called sci-kit-learn, and employ a handful of its machine learning algorithms to build clever models and make interesting forecasts.
Course Module
Module 1 – Importing Process of Datasets
- Comprehending the domain
- Knowing the Dataset
- Data science has a Python package
- How to import and export data in the programming language Python
- Fundamental insights from datasets
- Module 2 – How to clean and prepare the Data for analysis
- Finding and managing missing values
- How to format the data
- Normalizing datasets
- Binning
- Indicator variables
Module 3 – Encapsulating the Data Frame
- Descriptive Statistics
- Foundation of Grouping
- ANOVA
- Correlations
- Detailed Correlations
- Module 4 – Development
- Multiple as well as simple linear regressions
- Model assessment with the help of visualization
- Polynomial pipelines and regressions
- MSE and R-squared for in-sample examination
- Forecasting and Decision Making
Module 5 – Model Assessing
Model Assessment
- Over-fitting, selection, and under-fitting of models
- Ridge Regressions
- Grid Searching
- Refinement of models
Note – This course is entirely self-paced and can be started and completed at any place and at any time.
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4. Python Institute
As the name suggests, Python institute is called as one of the most popular institutes for learning data analysis, data science, and, it has one of the finest data analytics courses with Python. The program is hailed by students.
In addition to preparing you for the PAPD – Accredited Professional in Data Analytics Course with Python certification test, this advanced data analytics Course with Python educates you on how to utilize Python to carry out data mining, data visualization, and data analysis procedures.
The primary objective of the course is to acquaint you with the core concepts, key tools, and, best practices employed in the world of data science as well as the function of a data scientist throughout the full data analytics pipeline. You will be prepared by taking this course for employment and careers in data science and software development, which include roles like data analyst, software engineer, and, marketing analyst. Know how to visualise, assess, process, and, model data with the help of Python as you plunge into data science.
This transitional course will teach you the fundamental skills needed in the field of data analytics and will offer you the chance to dig into Python programming for data analysis. The course teaches you how to do programming in Python so that you can gather, clean, assess, sum up, and present data accurately and successfully. It will also expose you to the primary toolkits, approaches, and, concepts used by data analysts and data scientists.
Core Skills You Will Get to Know During the Course Are: –
- Data Analysis with Python
- Doing Computer Program
- Algorithmic Thinking
- Analytic Thinking
- Data Mining, Data Manipulating, Data Modeling, and, Data Visualization
- Best Practices employed in programming
- Statistical Operations
- Data-Oriented Decision-Making
Anyone and everyone interested in learning Python and the latest programming methods utilised for data visualization and data should take the course.
This Course Will Specifically Be Eligible for the: –
Ambitious programmers, newcomers to data science, and students interested in mastering programming to manipulate data and analyze data for amusement and job-related tasks
Learners seeking to acquire foundational skills and knowledge for entry-level positions like data scientists, software engineers, marketing analysts, data analysts, business intelligence analysts, and machine learning engineers
Experts in the data science field who are familiar with other programming languages and tools and who want to learn more about Python-related technologies or use it as the basis for data analytics
Budding programmers, sector experts, and students seeking to advance their education in the fields of machine learning and artificial intelligence, data engineering, data visualization, business analysis, data science, big data, and marketing analysis should consider learning Python and data analytics to obtain necessary skills
Team leaders, project managers, product owners, and product managers who want to control and communicate with data analytics and software development teams more successfully should comprehend the terminology and methods in data science
Prerequisites
This course is intended for students who are already acquainted with the fundamentals of procedural, organizational, functional, and object-focused programming as well as fundamental to intermedial Python programming concepts, such as functions, modules, loops, exceptions, containers, data types, and packages.
Experience Needed
To be eligible for this course, you must have completed Python Essentials 1 as well as Python Essentials 2, or a similar experience.
Here Are All the Things You Will Know After You Complete the Course: –
- You will be Designing, developing, debugging, executing, and refactoring Python scripts
- You will be thinking algorithmically to examine challenges and execute them as computer techniques
- You will be creating and processing arrays with the help of mathematical operations from the Numpy library
- You will be controlling and analyzing data using panda’s library
- You will be performing data visualizations with the help of the matplotlib plotting library
- You will comprehend the duties of a data scientist in every data analysis project
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5. Edx
Among several other platforms, Edx is also one of the top-ranked platforms for pursuing programs related to data science, data analytics, and data analytics courses with Python. Besides many of its highly-rated courses, thorough data analytics with Python can also be considered.
Course Name
Examining Data with the help of Python
In this advanced data analytics course with Python, you will start from the fundamentals of Python to discovering various forms of data. You will know the data analysis preparation process, carry out simple statistical examinations, create useful data visualizations, forecast upcoming trends from data, and many more.
During the Course You Will Get to Know the Following:
- Importing data sets
- Cleaning and preparing data for analysis
- Controlling pandas’ data frame
- Summing up data
- Building machine learning models with the help of scikit-learn
- Building data pipelines
The Delivery of the Data Analysis with Python Course Consists of Lectures, Practical Laboratories, and Projects. It Has the Following Components:
Data Analysis libraries: You will learn to use Pandas Data frames, multi-dimensional arrays, Sci-Py libraries, and, Numpy, to work with various datasets. You will learn about the open-source library, pandas, which will be then used to load, control, inspect, and visualize interesting datasets. Following that, you will be introduced to a different open-source library called sci-kit-learn and employ a handful of its machine-learning algorithms to build clever models and make interesting forecasts.
Course Module
Module 1 – Importing Process of Datasets
- Comprehending the domain
- Knowing the Dataset
- Data science has a python package
- How to import and export data in the programming language Python
- Fundamental insights from datasets
Module 2 – How to clean and Prepare the Data for Analysis
- Finding and managing missing values
- How to format the data
- Normalizing datasets
- Binning
- Indicator variables
Module 3 – Encapsulating the Data Frame
- Descriptive Statistics
- Foundation of Grouping
- ANOVA
- Correlations
- Detailed Correlations
Module 4 – Development
- Multiple as well as simple linear regressions
- Model assessment with the help of visualization
- Polynomial pipelines and regressions
- MSE and R-squared for in-sample examination
- Forecasting and Decision Making
Module 5 – Model Assessing
Model Assessment
- Over-fitting, selection, and under-fitting of models
- Ridge Regressions
- Grid Searching
- Refinement of models
6. Great Learning
When we mentioned the likes of Coursera, Udemy, and, others, then there is no way that we will forget about Great Learning. Great Learning is regarded as a fast-rising education platform that provides courses in various fields, including IT, data science, data analytics, and, many more. This platform is offering one of the top data analytics courses with Python.
Boost your career in the highly sought-after IT field of data analysis. Enroll in its highly-advanced data analytics with Python to find out more about Python and its data analysis libraries. Learn various methods and tools to use when working with these libraries.
You will learn about a crucial concept in this course: data analysis with the aid of Python. Your trainer will assist you in using a variety of Python libraries to examine various datasets within a Python Jupyter Notebook. You will work with a variety of datasets, including those related to the IPL, Marvel superheroes, and, football. Several captivating visualizations that you can utilize to present the examined data will be shown to you later. Additionally, you will become acquainted with the many dataset types that may be loaded into Notebook using Python. After completing the quiz, you will also receive a certificate of completion for the course. All these features make it one of the best Data Analytics courses With Python.
Course Features
- Free access throughout your life
- Learn at any place and at any moment
- You will get a certificate for completion of your course
- Make a statement to your network of colleagues
- Two hours of self-paced video lessons and lectures
Frequently Asked Questions on data analytics courses with Python.
Q1. Does learning Python for data analysis make sense?
Yes, data analysis is one of the hottest topics right now and one of the most sought-after skills. To progress in your career in data analytics, it is beneficial to enroll in this course.
Q2. Why is Python so well-liked for data analysis?
Python is a well-liked programming language that is utilized for the development of software, creating some valuable Artificial Intelligence tools. Because it makes it possible to build and control data structures rapidly, Python is also utilized for data analysis. Its prominence in data analysis is primarily due to this.
Q3. Who is eligible to enroll in this course on data analysis?
This course is open to anyone interested in learning Python and Data Analysis.
Q4. What skills and knowledge will I acquire after finishing this Data Analysis course?
You will comprehend the Jupyter Notebook’s user interface during the course. Additionally, you will learn about the many Python libraries used for data analysis. You will learn Python, Pandas, Seaborn, Matplotlib, and, Data Analytics after finishing this course.
Q5. For what positions do you need to know Python for Data Analysis?
Data Engineer, Data Analyst, Data Scientist, Supply chain management, Python Developer, and other positions all require proficiency with Python.
Conclusion on data analytics courses with Python.
Having mentioned the necessity to learn Python, it is high time that you start choosing the institute that provides the top data analytics courses with Python and aligns with your preferences. Moreover, with time employers are only going to demand that you learn Python so that you can perform data analysis with ease.
