Online Classroom Batches Preferred
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekend Regular
(Class 3hr - 3:30Hrs) / Per Session
Weekend Fasttrack
(Class 4:30Hr - 5:00Hrs) / Per Session
No Interest Financing start at ₹ 5000 / month
Skills You Will Gain
- Big Data, HDFS
- YARN, Spark
- MapReduce
- PIG, HIVE
- Mahout, Spark MLLib
- Solar, Lucene
- Zookeeper
Data Science with Python Course Key Features 100% Money Back Guarantee
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5 Weeks Training
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A professional application developer is an irreproachable source code creator of the software. Technoscientifically, application developers involves in the end-to-end software development life cycle. They create, test, deploy, and help to upgrade software as per the requirement of clients. They are often rewarded with substantial pay raises as shown below.
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Data Science with Python Course Curriculam
Trainers Profile
Trainers are certified professionals with 11+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Data Science with Python Certification Course are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Pre-requisites
Following is sufficient for basics of Data Science with Python technologies :
Syllabus of Data Science with Python Online Course Download syllabus
- What is Data Science?
- What does data science involves?
- Life cycle of Data Science
- Tools of Data Science
- Introduction to Python
- Introduction to python
- Software installation
- Basic operators and functions
- Data types with python
- Conditional statement
- Introduction to Numpy
- Introduction to numpy arrays
- How to Access Array Elements?
- Indexing, Slicing, Iteration, Indexing with Boolean Arrays
- Measures of Central Tendency
- Measures of Dispersion
- Descriptive Statistics
- Probability Basics
- Marginal Probability
- Bayes Theorem
- Probability Distributions
- Hypothesis Testing
- Understand Time Series Data
- Visualizing Time Series Components
- Exponential Smoothing
- Holt's Model
- Holt-Winter's Model
- Introduction to Pandas
- Defining data structures
- Understanding Dataframes
- Importing Data from various sources
- Missing values
- Data Operations
- File read operations
- Descriptive statistics
- Create plots like scatter plot
- Histogram, Bar graph, Pie chart using Matplotlib Grids
- Machine learning Process Flow
- Machine learning categories
- Feature selection and extraction in machine learning
- Supervised learning algorithms
- Simple linear Regression
- Applications of linear regression
- Building regression models using python
- Process to implement linear regression
- Coefficient of determination (R- Squared)
- Accuracy of model
- Multiple linear Regression
- Parsing HTML and XML
- Navigating the document
- Handling CSV files
- Parsing JSON into Python
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- Our mock interviews will be conducted by industry experts with an average experience of 7+ years. So you’re sure to improve your chances of getting hired!
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Data Science with Python Training Objectives
- Taking the Data Science with Python certification course will help you better understand the fundamentals of data science using the Python programming language. Data mining, machine learning, natural language processing, and data analysis are among the topics covered. You will have built a number of models at the end of the course and be able to put your data science knowledge to work. Practical assignments throughout the course allow you to apply what you've learnt. You will also gain knowledge about the abilities needed to be a successful data scientist
- Data analyst.
- Machine learning engineer.
- Business intelligence analyst.
- Data engineer.
- Python's ability to combine programming, data analysis, visualization, and machine learning has led to a fast expanding area known as "data science." Python's libraries and tools are becoming even more important as big data usage increases since they can handle a variety of jobs from analytics to streaming data. As a result, Python's position in data science is expected to grow in importance over time since it enables analysts to quickly prototype ideas while also assisting data engineers in managing and storing enormous amounts of data. Python's use in data science has a promising future as a result.
- Installing and putting Python is a ought to earlier than starting a statistics technology exercise.
- Become Familiar with Python: Before the use of Python for facts science, you need to be able to understand and create a little amount of Python code.
- Download and Install Data Science Libraries: When you experience assured the use of Python, you may start obtaining and using records technological know-how libraries.
- Finish Data Science Challenges: To practice your statistics technology capabilities, you could end some of Data Science Challenges.
- Python fundamentals and advanced modules.
- Working with data formats like CSV, JSON, XML, XLS, HTML.
- Build data-driven applications.
- Machine learning and predictive analytics.
- Data visualization with Matplotlib, Seaborn, and Folium.
- The Data Science with Python course offers numerous advantages, including the ability to gain a greater understanding of the field of Data Science, to learn how to work with the Python programming language, to acquire the skills to clean, process, and analyze data, and to be able to create highly advanced data visualizations. The course also provides a comprehensive overview of the technologies and processes involved in building and deploying data-driven projects.
- Become acquainted with the usage of quite a few Python libraries, consisting of SciPy, NumPy, Pandas, Scikit-research, Matplotlib, and Seaborn, to research records.
- Use Python to improve your facts analysis, mining, visualization, decision-making, and predictive analytics abilities.
- Be familiar with the essential ideas at the back of AI algorithms and facts science.
- Have a working know-how of information pipeline advent, information extraction, and records transformation.
- In order to successfully participate in the data science with Python course, you should be familiar with basic programming concepts, such as variables, functions, data types, loops, and list comprehensions. Additionally, students should have experience working with Python, including how to create objects, manipulate data, build classes, and use libraries. Prior knowledge of working with databases, either relational or non-relational, would also be beneficial.
- Data science with Python is a powerful tool for data analysis. It is used to analyze, visualize, and process large sets of data to gain insights and develop predictive models for making predications about future events and trends. It is also used to build machine learning models for predictive analytics and to create recommendation engines.
- Someone who has been educated in data science using Python most frequently gets $ 136,982 per year of employment.
Exam & Certification
- Participate and Complete One batch of Data Science with Python Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Data Science with Python course
- Successful completion and evaluation of any one of the given projects
- Oracle Certified Associate (OCA)
- Oracle Certified Professional (OCP)
- Oracle Certified Expert (OCE)
- Oracle Certified Master (OCM)
- Learn About the Certification Paths.
- Write Code Daily This will help you develop Coding Reading and Writing ability.
- Refer and Read Recommended Books Depending on Which Exam you are Going to Take up.
- Join LearnoVita Online Training Course That Gives you a High Chance to interact with your Subject Expert Instructors and fellow Aspirants Preparing for Certifications.
- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.

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Software Testing, CapgeminiData Science with Python Course FAQ's
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- All training comes with multiple projects that thoroughly test your skills, learning, and practical knowledge, making you completely industry-ready.
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