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Data Science with Python Certification Course
Data Science with Python Certification Course
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Why enroll for Data Science with Python course?
Data Science with Python Certification Training Benefits
Data science job opportunities are projected to increase by 30% annually, and proficiency in Python programming and data science can unlock vast job prospects. The rise in demand for skilled data scientists and machine learning engineers has prompted more businesses to incorporate machine learning into their operations and help companies analyze and process data, leading to quick and effective decision-making. Obtaining a Data Science and ML Certification from Edureka is the optimal way to secure a lucrative job in this field.
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Why Data Science with Python course from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
About your Data Science with Python course
Data Science with Python Skills Covered
- Python Programming
- Statistical Foundations
- Data Analysis and Visualization
- Supervised and Unsupervised Machine Learning
- Database Integration with Python
- Data Visualization Using Tableau
Data Science with Python Tools Covered
Data Science with Python Course Curriculum
Curriculum Designed by Experts
Introduction to Data Science and ML using Python
16 Topics
Topics:
- Overview of Python
- The Companies using Python
- Different Applications where Python is Used
- Discuss Python Scripts on UNIX/Windows
- Values, Types, Variables
- Operands and Expressions
- Conditional Statements
- Loops
- Command Line Arguments
- Writing to the Screen
- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
Hands-on:
- Creating “Hello World” code
- Variables
- Demonstrating Conditional Statements
- Demonstrating Loops
Skills You will Learn:
- Basics of Python Programming
- Command Line Parameters and Flow Control in Python
Data Handling, Sequences and File Operations
12 Topics
Topics:
- Data Analysis Pipeline
- What is Data Extraction?
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Python files I/O Functions
- Numbers
- Strings and related operations
- Tuples and related operations
- Lists and related operations
- Dictionaries and related operations
- Sets and related operations
Hands-on:
- Tuple - properties, related operations, compared with the list
- List - properties, related operations
- Dictionary - properties, related operations
- Set - properties, related operations
Skills You will Learn:
- Taking input from the user and performing operations on it
- Data types in Python
Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions
13 Topics
Topics:
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values
- Lambda Functions
- Object Oriented Concepts
- Standard Libraries
- Modules Used in Python
- The Import Statements
- Module Search Path
- Package Installation Ways
- Errors and Exception Handling
- Handling Multiple Exceptions
Hands-on:
- Lambda function in Python
- Errors and Exceptions in Python
- Packages and Modules in Python
- Functions - Syntax, Arguments, Keyword Arguments, Return Values
- Sorting - Sequences, Dictionaries, Limitations of Sorting
Skills You will Learn:
- Object Oriented Concepts
- Python Functions, Standard Libraries and Modules
- Handling Exceptions in Python
Introduction to NumPy, Pandas, and Matplotlib
13 Topics
Topics:
- Data Analysis
- NumPy - arrays
- Operations on arrays
- Indexing, slicing, and iterating
- Reading and writing arrays on files
- Pandas - data structures & index operations
- Reading and Writing data from Excel/CSV formats into Pandas
- Metadata for imported Datasets
- Matplotlib library
- Grids, axes, plots
- Markers, colors, fonts, and styling
- Types of plots - bar graphs, pie charts, histograms
- Contour plots
Hands-on:
- NumPy library - Creating NumPy array, operations performed on NumPy array
- Pandas library - Creating series and data frames, Importing and exporting data
- Matplotlib library - Using Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot
Skills You will Learn:
- Basic Functionalities of the NumPy library in Python
- Basic Functionalities of the Pandas library in Python
- Basic Functionalities of the Matplotlib library in Python
Data Manipulation
5 Topics
Topics:
- Basic Functionalities of a data object
- Merging of Data objects
- Concatenation of data objects
- Types of Joins on data objects
- Exploring and analyzing datasets
- Analysing a dataset
Hands-on:
- Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(), GroupBy operations, Aggregation, Concatenation, Merging and joining
Skills You will Learn:
- Performing data manipulation using various functionalities of the Pandas library in Python
Introduction to Machine Learning with Python
6 Topics
Topics:
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Linear regression
- Gradient descent
Hands-on:
- Linear Regression – Boston Dataset
Skills You will Learn:
- Machine Learning concepts
- Machine Learning types
- Linear Regression Implementation
Supervised Learning - I
6 Topics
Topics:
- What are Classification and its use cases?
- What is a Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?
Hands-on:
- Implementation of Logistic Regression, Decision Tree, Random Forest algorithms
Skills You will Learn:
- Supervised Learning concepts
- Implementing various Supervised Learning algorithms
- Evaluating model output
Dimensionality Reduction
6 Topics
Topics:
- Introduction to Dimensionality
- Why Dimensionality Reduction
- PCA
- Factor Analysis
- Scaling dimensional model
- LDA
Hands-on:
- Implementing PCA
- Scaling dimensional model
- Implementing LDA
Skills You will Learn:
- Implementing Dimensionality Reduction Technique
Supervised Learning - II
8 Topics
Topics:
- What is Naïve Bayes?
- How Naïve Bayes works?
- Implementing Naïve Bayes Classifier
- What is a Support Vector Machine?
- Illustrate how Support Vector Machine works
- Hyperparameter Optimization
- Grid Search vs. Random Search
- Implementation of Support Vector Machine for Classification
Hands-on:
- Implementation of Naïve Bayes, SVM algorithms
Skills You will Learn:
- Supervised Learning concepts
- Implementing various Supervised Learning algorithms
- Evaluating model output
Unsupervised Learning
7 Topics
Topics:
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How does the K-means algorithm works?
- How to do optimal clustering
- What is C-means Clustering?
- What is Hierarchical Clustering?
- How does Hierarchical Clustering work?
Hands-on:
- Implementing K-means Clustering
- Implementing Hierarchical Clustering
Skills You will Learn:
- Unsupervised Learning concepts
- Implementation of various Clustering techniques
Association Rules Mining and Recommendation Systems
7 Topics
Topics:
- What are Association Rules?
- Association Rule Parameters
- Calculating Association Rule Parameters
- Recommendation Engines
- How do Recommendation Engines work?
- Collaborative Filtering
- Content-Based Filtering
Hands-on:
- Implementing Apriori Algorithm
- Performing Market Basket Analysis
Skills You will Learn:
- Data Mining using Python
- Recommender Systems using Python
Reinforcement Learning (Self-Paced)
9 Topics
Topics:
- What is Reinforcement Learning?
- Why Reinforcement Learning?
- Elements of Reinforcement Learning
- Exploration vs. Exploitation dilemma
- Epsilon Greedy Algorithm
- Markov Decision Process (MDP)
- Q values and V values
- Q – Learning
- Values
Hands-on:
- Calculating Reward
- Discounted Reward
- Calculating Optimal quantities
- Implementing Q Learning
- Setting up an Optimal Action
Skills You will Learn:
- Implementing Reinforcement Learning using Python
- Developing Q Learning model in Python
Time Series Analysis (Self-Paced)
10 Topics
Topics:
- What is Time Series Analysis?
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACF
Hands-on:
- Checking Stationarity
- Converting non-stationary data to stationary
- Implementing Dickey-Fuller Test
- Plotting ACF and PACF
- Generating the ARIMA plot
Skills You will Learn:
- TSA Forecasting in Python
Model Selection and Boosting
7 Topics
Topics:
- What is Model Selection?
- Need for Model Selection
- Cross Validation
- What is Boosting?
- How do Boosting Algorithms work?
- Types of Boosting Algorithms
- Adaptive Boosting
Hands-on:
- Performing Cross Validation
- Implementing AdaBoost using Python
Skills You will Learn:
- Performing Model Selection
- Boosting algorithms using Python
Statistical Foundations (Self-Paced)
8 Topics
Topics:
- What is Exploratory Data Analysis?
- EDA Techniques
- EDA Classification
- Univariate Non-graphical EDA
- Univariate Graphical EDA
- Multivariate Non-graphical EDA
- Multivariate Graphical EDA
- Heat Maps
Hands-on:
- Implementing Graphical EDA Techniques
- Implementing Non-Graphical EDA Techniques
Skills You will Learn:
- Performing EDA on the dataset(s) in Python
Database Integration with Python (Self-Paced)
22 Topics
Topics:
- Basics of database management
- Python MySql
- Create database
- Create a table
- Insert into table
- Select query
- Where clause
- OrderBy clause
- Delete query
- Drop table
- Update query
- Limit clause
- Join and Self-Join
- MongoDB (Unstructured)
- Insert_one query
- Insert_many query
- Update_one query
- Update_many query
- Create_index query
- Drop_index query
- Delete and drop collections
- Limit query
Hands-on:
- CRUD operations using Python MySql and MongoDB
Skills You will Learn:
- Database management systems
- Database Integration with Python
- Working of database applications
Data Connection and Visualization in Tableau (Self-Paced)
9 Topics
Topics:
- Data Visualization
- Business Intelligence tools
- VizQL Technology
- Connect to data from the File
- Connect to data from the Database
- Basic Charts
- Chart Operations
- Combining Data
- Calculations
Hands-on:
- Connecting to data from File, Database, and Server
- Performing operations on Hierarchies, Data Granularity and Highlighting feature
- Creating calculated fields using basic functions
- Defining LOD expressions
- Creating Parameters
- Performing User Input and What-if analysis
Skills You will Learn:
- Data Distribution using various charts in Tableau
- Combining Data using Joins, Unions and Data Blending
- Sorting, Filtering and Grouping Techniques
- Table Calculations in Tableau
Advanced Visualizations (Self-Paced)
10 Topics
Topics:
- Trend lines
- Reference lines
- Forecasting
- Clustering
- Geographic Maps
- Using charts effectively
- Dashboards
- Story Points
- Visual best practices
- Publish to Tableau Online
Hands-on:
- Analyzing data using techniques including Forecasting, Trend Lines, Reference Lines, Clustering, and Geographic Maps
- Building Dashboard Layout and Formatting
- Building Story points
Skills You will Learn:
- Advanced visualization techniques in Tableau
- Building Dashboards and Stories in Tableau
In-Class Project (Self-Paced)
1 Topics
Topics:
- Predict the species of Plant
Hands-on:
- Analyze the data
- Predict the plant species
Skills You will Learn:
- Data Pre-processing
- Feature Engineering
- Implementation of Machine Learning Algorithm
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Data Science with Python Course Description
About Data Science with Python Certification Course
Edureka’s Data Science with Python Certification Training is designed to make you grasp the concepts of Data Science and Machine Learning. As a data scientist, you will learn the importance of machine learning and its implementation in the Python programming language. You will be able to automate real-life scenarios using machine learning algorithms, and towards the end of this course, we will be discussing various practical use cases of machine learning with the Python programming language to enhance your learning experience. Edureka offers the best Data Science with Python training online for those who want to be the best in Python. Enroll now in Edureka's online Data Science with Python Certification to get trained by industry experts.
Why Learn Data Science using Python?
Python has been one of the premier, flexible, and powerful open-source languages that are easy to learn, easy to use, and powerful libraries for data manipulation and analysis. It has been used for over a decade in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It continues to be a favorite option for data scientists who build and use machine learning applications and other scientific computations. Python cuts development time in half with its simple-to-read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built-in debugger. It has evolved into the most preferred Language for Data Analytics. The increasing search trends also indicate that it is the Next Big Thing and a must for professionals in the data analytics domain.
What are the objectives of our Data Science with Python Training?
After completing this Data Science with Python Certification course, you will be able to:
Edureka offers the best online course for Python data science and ML. Enroll now in our Data Science with Python training and get a chance to learn from industry leaders.
Who should go for this Python Data Science and ML online course?
What are the prerequisites for this Data Science with Python Training?
The prerequisites for Edureka's Python Data Science and ML course training include the fundamental understanding of Computer Programming Languages.
How will I execute the practicals in this online Data Science with Python course?
You will do your assignments and case studies using Jupyter Notebook, which is already installed on your Cloud LAB environment (access it from a browser). The access credentials are available on your LMS. Should you have any queries, the 24*7 Support Team will promptly assist you.
Data Science and ML with Python Certification Projects
Industry: Automobile
Riding the digital wave, India's used car market is set to grow at a compounded annual growth of 11% and likely to touch sales of up to 8.3 million units by FY26 as more people h....Industry: Healthcare
Heart Disease is among the most prevalent chronic diseases in the United States, impacting millions of Americans each year and exerting a significant financial burden on the econ....Data Science with Python Certification
A skilling ecosystem focused on emerging technologies, powered by a partnership between the Ministry of Electronics and Information Technology, the Government of India, NASSCOM, and the IT industry. It seeks to propel India to become a global hub of talent in emerging technologies. FutureSkills Prime is one of the lighthouse schemes under the Government’s Trillion Dollar Digital Economy initiative.
You need to complete the modules successfully to earn a Joint Co-Branded Certificate of Completion by NASSCOM FutureSkills Prime and Edureka. On completing the course, the Learner is eligible for Government of India (GOI) incentives after successfully clearing the mandatory NASSCOM Assessment for which the learner will be awarded a NASSCOM certification. For more details please visit: https://futureskillsPrime.in/govt-of-India-incentives.
- First of its kind government and industry partnership to drive a national skilling ecosystem for digital technologies.
- End-to-end skilling from assessment to certification.
- Affordable, credible content handpicked by industry leaders.
- Speed up learning with bite-sized course modules.
- Certifications recognized by the industry
GoI Incentive can be claimed by all Indian Nationals above 18 years of age. The current programme covers beneficiaries divided into the following broad categories:
- IT employees in IT Firms and Non-IT firms
- Non-IT employees aspiring to use new and emerging technologies in their respective domains
- Employees whose skills for a particular job have become outdated.
- Central Govt. & State Govt. Employees including employees of PSUs & Autonomous bodies (Govt. Employees)
- Fresh Recruits who are yet to take up a job, as well as undergoing/selected for internship & Apprenticeship roles in IT/ ITeS
- Enrolment in the Edureka portal
- Successful completion of course modules
- Quizzes, assignments and certificate project submission
- Assignment and project evaluation by Edureka
- Joint co-branded certificate of participation from NASSCOM and Edureka
- Sign up on the FutureSkills Prime platform for mandatory FutureSkills Prime assessment
- SSC Certificate issuance by Futureskills Prime on successful completion
- Avail for GOI incentives upon successful completion of FutureSkills Prime assessment
To unlock the Edureka’s Data Science with Python Training course completion certificate, you must ensure the following:
- Completely participate in this Edureka’s Data Science with Python Training Course.
- Evaluation and completion of the quizzes and projects listed.
Yes, Data Scientist is a good career option for those interested in working with data and extracting insights from it. With the explosive growth of data in recent years, the demand for skilled data scientists has increased significantly. As a Data Scientist, one can work in a variety of industries such as healthcare, finance, marketing, and more. The job typically requires a strong foundation in statistics, machine learning, and programming skills, as well as a good understanding of business and domain knowledge. Data Scientist is responsible for collecting, analyzing, and interpreting large and complex data sets to inform business decisions and strategies. Overall, data science is a challenging and rewarding career option with a promising outlook for the future.
Yes, Machine Learning Engineer is a good career option for those interested in working with machine learning algorithms and implementing them in real-world applications. Machine learning is a rapidly growing field with increasing demand for professionals who can build and deploy machine learning models to automate tasks and extract insights from large amounts of data. As a Machine Learning Engineer, one can work in a variety of industries such as healthcare, finance, e-commerce, and more. The job typically requires a strong foundation in machine learning, programming skills, and a good understanding of software engineering principles. Overall, machine learning engineering can be a challenging and rewarding career option with a promising outlook for the future.
To learn data science and machine learning as a beginner, one can start by learning Python programming and then move on to data analysis. After understanding data analysis, one can learn the basics of machine learning, and apply machine learning algorithms to real-world problems. Edureka’s Data Science with Python Certification Training provides a structured learning experience that helps beginners gain practical experience and develop the skills necessary to become proficient in data science and machine learning.
Data Science with Python Certification provides a strong foundation in data science, machine learning, and Python programming. This certification is valuable for several reasons:
- Demonstrates Mastery of Key Skills: Certification indicates that an individual has a strong understanding of data science concepts, machine learning techniques, and Python programming skills.
- Improves Job Prospects: Data science and machine learning are high-growth industries, and certification can improve job prospects by demonstrating expertise in these areas.
- Increases Earning Potential: Certified data scientists and machine learning engineers often earn higher salaries compared to their non-certified peers.
- Enhances Credibility: Certification is a recognized indicator of expertise and can enhance an individual's credibility in the field.
- Keeps Skills Up-to-Date: Data science and machine learning are constantly evolving fields, and certification requires individuals to stay up-to-date with the latest technologies and techniques.
- Enables Career Advancement: Certification can enable individuals to advance their careers by demonstrating mastery of key skills and increasing their value to their organization.
Data Science with Python Certification can open up various job roles in the field of data science and machine learning. Some of the common job roles available after completing this certification include:
- Data Analyst: A data analyst collects, analyzes, and interprets large datasets to help businesses make informed decisions.
- Machine Learning Engineer: A Machine Learning Engineer is responsible for designing, building, and deploying machine learning models that can automate certain tasks.
- Data Scientist: A Data Scientist is responsible for analyzing and interpreting complex data to extract insights and build predictive models.
- Business Intelligence Analyst: A Business Intelligence Analyst is responsible for analyzing data to provide insights that can help businesses make informed decisions.
- AI Architect: An AI Architect is responsible for designing and implementing AI systems, including machine learning algorithms and neural networks.
- Research Scientist: A Research Scientist is responsible for conducting research and experiments to develop new machine learning algorithms and techniques.
You do not need a coding background to enroll in this Data Science with Python course. The course begins with basic modules in which we cover the fundamentals of Python coding. In fact, you do not need prior knowledge in data science or machine learning either. All relevant topics are a part of this course from scratch.
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Data Science with Python Training Course FAQs
What is Python for Data Science?
Data is all around us, and data science will help extract the information. Data science has many applications, and python can be used to implement them. Python is a generic language that can be used to build websites, backend APIs, and scripting. Python's built-in libraries, frameworks, and tools can be used to perform various operations in data science.
Why should I learn Data Science with Python Course?
Python is definitely one of the most popular languages in Data Science, which can be used for data analysis, manipulation, and visualization. It has access to many Data Science libraries, making it the perfect language for developing applications and implementing algorithms.
Where Can I Learn Python for Data Science?
Although there are many free learning resources available, finding one that teaches data science very well is recommended. You should choose a platform that will teach you interactively and has a curriculum designed to help you along your data science journey. Edureka is one such platform as we offer the best online Data Science with Python course for data science that will take you from beginner to data analyst in Python or data scientist.
Can I learn a Python for Data Science online?
Technology has made it easier and more efficient to learn online. It allows you to learn at your own pace without any barriers. Edureka's Data Science with Python certification course offers live classes and online access to study material from any location at any time. You will be able to grasp the key concepts quickly with our extensive and growing collection of tutorials, blogs, and YouTube videos. We offer a 24/7 support service to answer any questions you may have after your class ends.
Who are the instructors for the Python Data Science Course?
All the instructors at edureka! are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants of Python Data Science Training.
What is the Data Science with Python Course duration?
Data Science with Python Course can take between five and 10 weeks to learn basic Python programming concepts, including object-oriented programming and basic Python syntax. It is important to note that the time it takes for Python programming depends on your experience with web development, data science, and other related fields.
How will I execute practical’s in Edureka's Data Science with Python Certification Course?
You will do your Assignments/Case Studies using Jupyter Notebook, already installed on your Cloud Lab environment, whose access details will be available on your LMS. You will be accessing your Cloud Lab environment from a browser. For any doubt, the 24*7 support team will promptly assist you.
What if I have more queries with regards to Data Science using Python course?
Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co
What does a Data Science Expert do?
A Data Science Expert applies statistical, mathematical, and computational techniques to analyze and interpret large and complex datasets to extract insights, make predictions, and inform decision-making. They are skilled in programming languages like Python or R and use various tools and technologies such as machine learning algorithms, data visualization, and database systems to manipulate, process and analyze data. They may work in various industries such as finance, healthcare, marketing, etc.
What is CloudLab?
CloudLab is a cloud-based Jupyter Notebook which is pre-installed with Python packages on the cloud-lab environment. It is offered by Edureka as a part of the Python for Data Science Course where you can execute all the in-class demos and work on real-life projects in a fluent manner. You’ll be able to access the CloudLab via your browser which requires minimal hardware configuration. In case, you get stuck in any step, our support ninja team is ready to assist 24x7.
What skills should a Data Science Expert know?
A Data Science Expert should have a combination of technical and non-technical skills, including:
- Strong programming skills in languages like Python, R, and SQL.
- Proficiency in statistical analysis, machine learning, and data visualization techniques.
- Knowledge of data structures, algorithms, and database systems.
- Strong problem-solving skills and ability to work with large and complex datasets.
- Understanding of business processes and ability to communicate effectively with stakeholders.
- Knowledge of software engineering principles for building scalable and maintainable data pipelines.
- Continual learning mindset to keep up with the latest trends and technologies in the field.
Why is it essential to learn Data Science with Python course?
Python is preferred by data scientists over other languages because it has powerful machine learning libraries that can be used to build any machine learning algorithm. This allows for a better understanding of the current performance without sacrificing existing performance. These powerful frameworks allow data scientists to create the right neural networks. Python is the foundation of Google, YouTube and Instagram. It allows for multiple tasks to be automated and the use of these applications in various languages. The code is simple and well-documented. Many organizations are still not adopting a data-centric approach. The market lacks data literacy. To fill this gap in supply, you will need to study data science and its underlying areas by taking python data science training.
Which kind of projects will be a part of this Data Science with Python Certification Course?
Project Title: Consumer Complaint Resolution
Problem Statement: Predicting which complaints have a higher potential to be disputed and identifying systematic issues can help enhance the quality of communication and satisfactory resolution.
Does Edureka provide any free learning resource for Data Science with Python course?
If you are looking for free resources on Python for Data Science then read our blogs on Data Science tutorial, and Data Science Interview Questions.
What if I miss a Data Science with Python Course class?
You will never miss a lecture at edureka! You can choose either of the two options:
- View the recorded session of the class available in your LMS.
- You can attend the missed session in any other live batch.
What type of job can I get after completing a Data Science with Python course?
This is an industry where opportunities are plenty, so once you have the education and qualifications, the jobs are waiting for you. To name a few, some of the most common job titles for data scientists include:
- Business Intelligence Analyst
- Data Mining Engineer
- Data Architect
- Data Scientist
- Senior Data Scientist
Will I get placement assistance after Data Science with Python Training?
To help you in this endeavor, we have added a resume builder tool to your LMS. Now, you can create a winning resume in just 3 easy steps. You will have unlimited access to these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
What are the other Data Science courses offered by Edureka apart from this Course?
Can I attend a demo session before enrollment in Python for Data Science Course?
We have a limited number of participants in a live session of Data Science with Python course to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight into how the classes are conducted, quality of instructors and the level of interaction in a class.
Does Python help in Data Science?
Python has several built-in libraries, frameworks, and tools that can be used to implement various functions of data science. Python's syntax is much more understandable than other programming languages like Scala and R. It is a data science tool that allows you to explore data science concepts in the most effective way possible. This makes it a highly skilled language and makes it the ideal choice for the Data Science Field.
Is R better than Python for Data Science?
TIOBE, Stack Overflow, and RedMonk indicate that Python is the most popular programming language in the broader tech community. This doesn't necessarily make it better, but it does suggest that it is more popular and has a stronger community for support and development.
How is Python used in Data Science?
Python has become the most demanding language in the data science communities due to it's compatibility and easy to use syntax. You can learn even if you don’t have an engineering and science background. Its versatility and easy to understand makes Python the most sought after-skills that big organizations are looking for in a data science professional.
What will I get once I sign up for the Data Science with Python Course?
You can access all the Specialization courses when you sign up for the course. Once you have completed the work, you will receive a certificate added to your Accomplishments page. From there, you can print it or add it to LinkedIn. You can view and read the course content for free if you don't want to pay.
Which companies will hire me once I become a Python for Data Science professional?
Data science has grown by a substantial extent today. Companies in almost every industry are trying to have a data science team to help them use their data for the company’s progress.
Here, we have compiled a list of reputed companies that are currently hiring data scientists.
1. Sigmoid
2. Mindtree
3. LinkedIn
4. Paypal
5. Oracle
6. TCS
7. ZIGRAM
What are the popular cities where Edureka provides Data Science with Python courses?
Here is the list of cities where Edureka provides Data Science with Python Course:
What is the Avg Salary range for Data scientists in various countries?
The salary range for Data scientists in various countries according to a salary survey by Payscale, Glassdoor, and talent.com:
What are the cost/training fee for the Data Science with Python Course in other countries?
Find the cost of Data Science with Python Course in different countries:
What is the Average Salary of Data scientists across the world?
According to Payscale, the average salary for a Data Scientist with Python skills is $98307.
What are the other countries/cities where Edureka provides Data Science with Python courses apart from India and US?
Can you become a Data Scientist with Python only?
Although Python alone can be used to use data science in some instances, unfortunately for the corporate world, it's only part of the puzzle for companies to manage a large amount of data. Python is a fascinating coding language to master, especially for those who want to become data scientists. Its significance of it in data science should be considered and valued.
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