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Data Science with Python Training in San Jose
Data Science with Python Training in San Jose
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Why enroll for Data Science with Python course in San Jose?
Data Science with Python Certification Training Benefits in San Jose
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 in San Jose
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 in San Jose
Data Science with Python Tools Covered in San Jose
Data Science with Python Course Curriculum in San Jose
Curriculum Designed by Experts
Edureka’s Data Science with Python Training in San Jose will provide hands-on experience in Python programming. We offer live-instructor-led sessions which will help you in mastering the concepts involved in Python. With Edureka’s Data Science Python training in San Jose, you will learn the essential concepts of Python programming such as Data Structure, Data types, Strings, tuples, lists, basic operators and functions, GUIs, Multi-threading, Data Manipulation, Expressions, Networking, Lambda expressions and much more. Also, in this Data Science Python course, you will gain a thorough knowledge of Python OOPS concepts, modules, Django framework, Database programming, and connectivity to multiple data sources.
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
![skill]( "Hands On Experience")
- Creating “Hello World” code
- Variables
- Demonstrating Conditional Statements
- Demonstrating Loops
![skill]( "skill 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
![skill]( "Hands On Experience")
- Tuple - properties, related operations, compared with the list
- List - properties, related operations
- Dictionary - properties, related operations
- Set - properties, related operations
![skill]( "skill 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
![skill]( "Hands On Experience")
- 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
![skill]( "skill 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
![skill]( "Hands On Experience")
- 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
![skill]( "skill 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
![skill]( "Hands On Experience")
- Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(), GroupBy operations, Aggregation, Concatenation, Merging and joining
![skill]( "skill 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
![skill]( "Hands On Experience")
- Linear Regression – Boston Dataset
![skill]( "skill 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?
![skill]( "Hands On Experience")
- Implementation of Logistic Regression, Decision Tree, Random Forest algorithms
![skill]( "skill 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
![skill]( "Hands On Experience")
- Implementing PCA
- Scaling dimensional model
- Implementing LDA
![skill]( "skill 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
![skill]( "Hands On Experience")
- Implementation of Naïve Bayes, SVM algorithms
![skill]( "skill 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?
![skill]( "Hands On Experience")
- Implementing K-means Clustering
- Implementing Hierarchical Clustering
![skill]( "skill 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
![skill]( "Hands On Experience")
- Implementing Apriori Algorithm
- Performing Market Basket Analysis
![skill]( "skill 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
![skill]( "Hands On Experience")
- Calculating Reward
- Discounted Reward
- Calculating Optimal quantities
- Implementing Q Learning
- Setting up an Optimal Action
![skill]( "skill 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
![skill]( "Hands On Experience")
- Checking Stationarity
- Converting non-stationary data to stationary
- Implementing Dickey-Fuller Test
- Plotting ACF and PACF
- Generating the ARIMA plot
![skill]( "skill 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
![skill]( "Hands On Experience")
- Performing Cross Validation
- Implementing AdaBoost using Python
![skill]( "skill 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
![skill]( "Hands On Experience")
- Implementing Graphical EDA Techniques
- Implementing Non-Graphical EDA Techniques
![skill]( "skill 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
![skill]( "Hands On Experience")
- CRUD operations using Python MySql and MongoDB
![skill]( "skill 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
![skill]( "Hands On Experience")
- 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
![skill]( "skill 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
![skill]( "Hands On Experience")
- Analyzing data using techniques including Forecasting, Trend Lines, Reference Lines, Clustering, and Geographic Maps
- Building Dashboard Layout and Formatting
- Building Story points
![skill]( "skill 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
![skill]( "Hands On Experience")
- Analyze the data
- Predict the plant species
![skill]( "skill you will learn")
- Data Pre-processing
- Feature Engineering
- Implementation of Machine Learning Algorithm
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Data Science Python Training in San Jose Description
Edureka provides extensive Data Science with Python training in San Jose to assist you in mastering basics with sophisticated theoretical ideas such as writing scripts, sequence, and file procedures in Python while gaining practical experience with functional apps. This Data Science training is combined with hands-on tasks and live projects. Python is a common high-level, open-source programming language with a broad spectrum of apps for games and web applications in automation, big data, data science, and information analytics development. It's a versatile, powerful, object-oriented and interpreted language that you will learn during this Data Science with Python course in San Jose.
About Data Science with Python Certification Course
Edureka's Data Science with Python Certification Training teaches you the significance of data science, machine learning, and its application in Python. This course helps you gain proficiency in automating real-world scenarios with machine learning algorithms. Edureka offers the most exceptional online Data Science with Python training if you are dedicated to becoming proficient in Python. Seize the opportunity and enroll in an online Data Science with Python Certification Course now to benefit from the teaching of industry experts. Elevate your Python skills with us today.
What are the prerequisites for this Data Science with Python Course?
The prerequisites for Edureka's Python for Data Science Course include a fundamental understanding of Computer Programming Languages.
Why Learn Python for Data Science?
Python is one of the premier, flexible, and powerful open-source languages that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, It has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing. It's continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple-to-read syntax and easy compilation feature. The debugging program is a breeze in Python, and it has a built-in debugger. It has evolved as 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 Course?
After completing this Data Science with Python Certification course, you will be able to:
- Programmatically download and analyze data
- Learn techniques to deal with different types of data – ordinal, categorical, encoding
- Learn data visualization
- Using python notebooks, master the art of presenting step-by-step data analysis
- Gain insight into the 'Roles' played by a Machine Learning Engineer
- Describe Machine Learning
- Work with real-time data
- Learn tools and techniques for predictive modeling
- Discuss Machine Learning algorithms and their implementation
- Validate Machine Learning algorithms
- Explain Time Series and its related concepts
- Perform Text Mining and Sentiment analysis
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 for Data Science course?
- Freshers, Programmers, Developers, Technical Leads, Architects
- Developers aspiring to be a ‘Machine Learning Engineer'
- Analytics Managers who are leading a team of analysts
- Business Analysts who want to understand Machine Learning (ML) Techniques
- Information Architects who want to gain expertise in Predictive Analytics
- Professionals who want to design automatic predictive models
What is the Data Science with Python Course duration?
The duration of the Data Science with Python Course is approximately 5 to 10 weeks. Depending on their experience with web development, data science, and other related fields, it may vary from person to person.
What is the Data Science with Python Course fee?
The Data Science with Python course fee is INR 21995, but you can get it at a discounted price of INR 19795. You can get 10% OFF and save 2200. No Cost EMI Starts at 6,599 / month.
What topics are covered in Data Science with Python course syllabus?
Here is the list of topics covered in the Data Science with Python Course Syllabus:
- Introduction to Data Science and ML using Python
- Data Handling, Sequences and File Operations
- Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions
- Introduction to NumPy, Pandas, and Matplotlib
- Data Manipulation
- Introduction to Machine Learning with Python
- Supervised Learning - I
- Dimensionality Reduction
- Supervised Learning - II
- Unsupervised Learning
- Association Rules Mining and Recommendation Systems
- Reinforcement Learning (Self-Paced)
- Time Series Analysis (Self-Paced)
- Model Selection and Boosting
- Statistical Foundations (Self-Paced)
- Database Integration with Python (Self-Paced)
- Data Connection and Visualization in Tableau (Self-Paced)
- Advanced Visualizations (Self-Paced)
- In-Class Project (Self-Paced)
Data Science with Python Certification Course Projects in San Jose
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 in San Jose
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, Edureka provides a course completion certificate titled Python for Data Science Professional upon completion of the course and fulfillment of minimum requirements.
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.
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Python Data Science Training in San Jose FAQs
What is Python for Data Science?
Data is everywhere, and data science will help us extract it. Python has been used to implement numerous data science applications. Python is a generic programming language for creating websites, backend APIs, and scripting. Python's built-in libraries, frameworks, and tools can be used to execute various data science activities.
Why should I learn Data Science with Python Course?
Python is unquestionably one of the most popular data science languages, capable of data analysis, manipulation, and visualization. It has access to many of Data Science libraries, making it ideal for designing applications and applying algorithms.
Where Can I Learn Python for Data Science?
Yes, Edureka’s Data Science with Python course is available 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.
Who are the instructors of 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 for Data Science Course.
What is the Data Science with Python Course duration?
The duration of the Data Science with Python Course is approximately 5 to 10 weeks. Depending on their experience with web development, data science, and other related fields, it may vary from person to person.
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 does a Python 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.
What if I have more queries with regards to Data Science with Python course?
Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co
Which Data Science with Python course is best for beginners?
Edureka provides the best Python for Data Science course, which is well-suited for beginners and professionals. This course is designed by subject matter experts and provides hands-on experience with real-time projects and case studies. It helps you learn Python for Data Science from scratch.
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, Here are the required Data scientists skills:
- 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?
Data scientists prefer Python because it incorporates sophisticated machine learning libraries that can be used to build any machine learning method. This gives a better understanding of present performance without jeopardizing it. These robust frameworks allow data scientists to build the best neural networks possible. Python is at the heart of Google, YouTube, and Instagram. It enables the automation of many operations and the use of these apps in various languages. The code is straightforward and well-documented. Many firms still need to embrace a data-driven strategy. More data literacy is required in the market. It would be best if you studied data science and its underlying fields by taking Python for data science course to fill this supply shortage.
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.
Where can I learn Python for Data Science for free?
There are multiple free resources available online, like blogs, tutorials, and YouTube videos. You can explore these resources on the edureka website for free. These resources will help you learn the basics of Python data science concepts. However, to gain an in-depth understanding, it is advisable to enroll in a paid data science with Python course.
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?
Here are some most common job opportunities available after completing Dara Science with Python Course:
- 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 Data Science with Python 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.
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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