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Data Science AWS real time interview questions

1.How to deploy python code on AWS

Ans ::The AWS SDK for Python (Boto3) enables you to use Python code to interact with AWS services like Amazon S3

2.What id versioning in s3?

Ans : You can use S3 Versioning to keep multiple versions of an object in one bucket and enable you to restore objects that are accidentally deleted or overwritten. For example, if you delete an object, instead of removing it permanently, Amazon S3 inserts a delete marker, which becomes the current object version.

3.How to create crawler?

To create a crawler that reads files stored on Amazon S3 On the AWS Glue service console, on the left-side menu, choose Crawlers. On the Crawlers page, choose Add crawler. This starts a series of pages that prompt you for the crawler details. In the Crawler name field, enter Flights Data Crawler, and choose Next, submit info

4.HOW to create cluster?

From the navigation bar, select the Region to use. In the navigation pane, choose Clusters. On the Clusters page, choose Create Cluster. For Select cluster compatibility, choose one of the following options and then choose Next Step

5.what u did in athena?

Athena helps you analyze unstructured, semi-structured, and structured data stored in Amazon S3. Examples include CSV, JSON, or columnar data formats such as Apache Parquet and Apache ORC. You can use Athena to run ad-hoc queries using ANSI SQL, without the need to aggregate or load the data into Athena. Basically we do data validation by using Athena

6.what is ETL?

ETL stands for extract, transform, and load and is a traditionally accepted way for organizations to combine data from multiple systems into a single database, data store, data warehouse, or data lake

OR

ETL->

Extraction: Data is taken from one or more sources or systems. The extraction locates and

identifies relevant data, then prepares it for processing or transformation. Extraction allows

many different kinds of data to be combined and ultimately mined for business intelligence.

➢ Transformation: Once the data has been successfully extracted, it is ready to be

refined. During the transformation phase, data is sorted, organized, and cleansed.

For example, duplicate entries will be deleted, missing values removed or enriched,

and audits will be performed to produce data that is reliable, consistent, and usable.

➢ Loading: The transformed, high quality data is then delivered to a single, unified

target location for storage and analysis.

Data Bricks Interview Questions

1.What is Databricks, and how does it differ from other big data processing frameworks like Hadoop and Spark?

2.Can you walk us through the process of creating a new Databricks cluster and configuring it for your specific use case?

3.How do you optimize performance when working with large data sets in Databricks?

4.How do you handle data security in Databricks, especially when dealing with sensitive data?

5.What are some common data transformations and analyses you can perform using Databricks, and what are the advantages of using Databricks for these tasks?

6.Can you describe a time when you used Databricks to solve a challenging data problem, and how you went about tackling that problem?

7.How do you handle errors and debugging when working with Databricks notebooks or jobs?

8.How do you monitor and track usage and performance of your Databricks clusters and jobs?

9.Can you walk us through a typical workflow for developing and deploying a Databricks-based data pipeline?

10.What are some best practices for optimizing cost and resource utilization when working with Databricks clusters?

Real time interview questions

1.what are your data sources

Ans: my data sources are like S3 in that data lake or diff files like csv, excel, or database

2.what is the latency of your data

Ans: actually it depends on the business requirement sometimes we have to do weekly jobs sometimes we have to do monthly data pipeline

3.what is vol of your data in daily basis

Ans: Around 10 GB data is processing daily

4.how many table do you have in your storage

Ans: Actually i didn’t count it but it may be 300 or 400 or may be more than this

5.What are the transformation you are using in daily

Ans: we are using withcolumn, distinct, joins, union, date formatting, dropduplicates, filter

6.how do u use incremental data in your project or pipeline

Ans: incremental as, In pipeline we write data as per our batch date. we overwrite new data to the final table

7.where u r using partition tables

Ans: mostly we r using partition tables in target and its very imp to partition a table and we are doing it on batch date

because of its simple to query and also help in powerbi to process this query faster

8.what is your final file format and why u r using parquet format

Ans: we are using parquet format, and so we are using spark and parquet works better with spark and also it has lot of compressing

ability, and it also stored data in nested structured and columnar format.

9.how did u submit spark job

Ans:

https://sparkbyexamples.com/spark/spark-submit-command/

or

https://spark.apache.org/docs/latest/submitting-applications.html#:~:text=The%20spark%2Dsubmit%20script%20in,application%20especially%20for%20each%20one.

10.how u decide the parameter and resources to configure the spark job

Ans: it depends on file size if we processing a file is large, then we have to see the no. of executors then we have to see how can we increase executor core and memory so our data pipeline execute faster, but generally there are default set of parameter that we use

11.have u ever used repartition

Ans: Yes , but only a few times b’cos its very costly operation and it shuffles data in many partitions. So we are not using it on a daily basis.

12.what are the common error you face while running a datapipeline

Ans: Syntax error

  • Data type mismatch
  • Missing values or corrupted data
  • Lack of resources
  • Connection issue
  • Permission issue

 

13.how did you solve datapipeline issue

-correct the syntax

  • You can use data validation or data cleansing tools to correct data types and to handle missing values
  • You can optimize the performance of your pipeline by using efficient algorithms, reducing the size of data, or scaling up your computing resources. You can also monitor resource usage and adjust your pipeline accordingly
  • You can configure retries or error handling mechanisms in your pipeline to handle network or connection errors.
  • You can ensure that your pipeline has the necessary permissions to access data and perform operations by configuring access control and security mechanisms.

 

 

Happy Learning 

“We now accept the fact that learning is a lifelong process of keeping abreast of change. And the most pressing task is to teach people how to learn.” — Peter Drucker

Best Data Science Course In Thane

November 20, 2024 No Comments

Best Data Science Course In Thane With Placement 100% Placement Assistance | Live Online Sessions Data Science course : 4 Month Personalized Live Advance Data Science Training is taught by industry experts in a comprehensive & question-oriented format. Enroll Before: 07 December, 2024 Download Broucher Apply now 1000+ Students Trained 100% Placement Assistance 07 December, 2024 Start Date 0% EMI Available 7:30 AM – 9:30 AM Lecture Timings ( IST ) Key Highlights Of The Advance Data Science Course 100+ Hours Of Live Class 50+ Hours of Videos 1-on-1 Mentoring Sessions 20+ Industry Tools Mastery In-Class Live Presentations 3+ Live Projects & Practice Assignments 100% Placement Assistance Resume & Interview Training Get familiar with our online Python Data Science course syllabus. 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It is a rapidly growing field with high demand for skilled professionals across various industries, from healthcare to finance. With its blend of analytical, programming, and problem-solving skills, data science provides diverse job opportunities, competitive salaries, and the potential to make a significant impact in shaping business decisions and innovation. The Career Opportunities After Completing a Data Science Course in Thane Data Engineer: Building and maintaining data pipelines, and working with data storage solutions and tools such as AWS Glue, Redshift, and MySQL. Quantitative Analyst: Applying quantitative techniques to financial and business data, leveraging Python for data manipulation and analysis. Data Analyst: Performing data analysis and visualization using tools like Pandas, Matplotlib, and PowerBI, and creating actionable insights from data. Cloud Data Engineer: Working with cloud services and data pipelines, including AWS services like S3, EMR, and Athena. DevOps Engineer: Implementing CI/CD pipelines and managing version control with Git, focusing on automation and integration in data engineering projects. Click here for Data Analyst Course​ Tools You’ll Master Mentors Shamli Python Trainer 4+ Years ITExperience4+ Years Teaching Experience Govind Chavan Project Trainer 5+ Years IT Experience5+Teaching Experience Rushikesh Deshmukh Digital Marketing 3+ Years IT Experience2+Teaching Experience Bharat Kale Manual Trainer 5+ Years IT Experience5+Teaching Experience Our Learners Work At Enroll Now and get 5% Off On Course Fees Bug Spotter Reviews https://www.youtube.com/watch?v=Kpv8ladIgIs&ab_channel=BugSpotter Introduction to Data Science Data Science is a field focused on extracting meaningful insights from large sets of data to solve real-world problems. Imagine a detective using data to find patterns, trends, and valuable information that can help businesses make informed decisions. 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Best Data Science Course In Satara With Placement 100% Placement Assistance | Live Online Sessions Data Science course : 4 Month Personalized Live Advance Data Science Training is taught by industry experts in a comprehensive & question-oriented format. Enroll Before: 07 December, 2024 Download Broucher Apply now 1000+ Students Trained 100% Placement Assistance 07 December, 2024 Start Date 0% EMI Available 7:30 AM – 9:30 AM Lecture Timings ( IST ) Contact Now Key Highlights Of The Advance Data Science Course 100+ Hours Of Live Class 50+ Hours of Videos 1-on-1 Mentoring Sessions 20+ Industry Tools Mastery In-Class Live Presentations 3+ Live Projects & Practice Assignments 100% Placement Assistance Resume & Interview Training Get familiar with our online Python Data Science course syllabus. Download Broucher Syllabus for Data Science Course Online Advance Data Science Course in Solapur is designed to teach students the basics to the advanced level concepts of Python Data Science with practice assignments and offline in-class projects which helps them to get placed in MNC’s. 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November 20, 2024 No Comments

Best Data Science Course In Sambhaji Nagar With Placement 100% Placement Assistance | Live Online Sessions Data Science course : 4 Month Personalized Live Advance Data Science Training is taught by industry experts in a comprehensive & question-oriented format. Enroll Before: 07 December, 2024 Download Broucher Apply now 1000+ Students Trained 100% Placement Assistance 07 December, 2024 Start Date 0% EMI Available 7:30 AM – 9:30 AM Lecture Timings ( IST ) Contact Now Key Highlights Of The Advance Data Science Course 100+ Hours Of Live Class 50+ Hours of Videos 1-on-1 Mentoring Sessions 20+ Industry Tools Mastery In-Class Live Presentations 3+ Live Projects & Practice Assignments 100% Placement Assistance Resume & Interview Training Get familiar with our online Python Data Science course syllabus. Download Broucher Syllabus for Data Science Course Online Advance Data Science Course in Sambhaji Nagar is designed to teach students the basics to the advanced level concepts of Python Data Science with practice assignments and offline in-class projects which helps them to get placed in MNC’s. Term 1 In this term, you will learn how to ace Python Basics, Python OOPS and Python Libraries like Pandas, Matplotlib, Numpy, etc…   Module 1 :- Python Python Basics :  Why python Python IDE Basics of programming Variables , Data Types Conditional statements Loops Logical Thinking Data Structures Functions and types of arguments Lambda Functions memory Management garbage collector Copies – shallow copy, deep copy Higher Order Functions – Map , Reduce , Filter Iterable , Iterator , generator Exception handling Programming interview questions Python OOPS : Class constructor and its types , Destructor Types of variables – instance , static Inheritance – Single , Multiple , Multilevel , Hierarchical polymorphism duck typing Overloading – method , Operator , constructor overriding – method , Constructor Super Function Encapsulation access Modifiers Abstraction monkey patching Module 2:- Python Libraries Pandas : Introduction to PandasSeries Data Structure Data Frame Data Structure Merging DataFrame Read Complex CSV , JSON , excel Files using pandas Write to File Data Frame Manipulation – head , Tail , Describe , shape ,Drop , inplace loc & I=iloc Apply Function Value count Add Column Add Row To DataFrame – using concat,Append Order By Operation Sort Values Group by operation Pivot Table Date/ Time Functionality Example Manipulating DataFrame Matplotlib : line graph bar Plot scatter plot pie chart other function Numpy : Introduction to Numpy Creating Arrays , Indexing , Slicing Data Types Copy vs View Array Shape & Reshape Arrays Split & Joins Arrays Filter Seaborn Model Data Engineering in Python : Handling Missing Data Techniques to inpute missing Values Meaningful Data transformation Encoding DataData Visualization in Python Read Json , CSV’s, excels Term 2 In this term, you will learn how to ace MySQL, AWS, Tools & IDE’S Module 3:- SQL/ETL MYSQL : DBMS & RDBMS Data Types DQL DDL DML TCL DCL Key Constraints Operators Clouses Aggregate Functions Indexes Views Triggers JOINS Sub Queries & Nested Queries   Module 4:- AWS Use of AWS Cloud computing models S3 AWS Data Pipeline EMR AWS Glue Athena Redshift Module 5:- Tools Azure devops : Use Of Devops CI/ CD Pipeline work item sprints repository state of task Repose Clone pull request Git : Use of Git feature branch clone Add Commit Push IDE : PowerBI : Dashboards Application DBeaver : Connection Process DB Manipulation Jupyter Notebook : Google Colaboratory Pycharm Term 3 In this term, you will learn how to ace Framework and industry projects Module 6:- Framework Pyspark : Use of Pyspark For Data Science Spark Session & RDD Timestamp Schema Parallelize Broadcast Variable Create DataFrame Transformations & actions Empty DataFrame Structure type and structure field Select Collect WithColumn Where & Filter Drop & Drop Duplicate orderby and sortby Groupby Joins union and union all union byname map , flatmap Sample by vs Sample Pivote maptype Aggregate Functions Windows Function Read and Write in CSV When Split collect Row number dense rank Module 7:- Final Projects and course wrap-up 1) E-Commerce 2) Banking Domain 0 + HOURS OF LIVE LEARNING 0 + Live Projects 0 + HOURS OF VIDEO LEARNING Download Detailed Data Science Course Syllabus & Trainer List Get Syllabus The Career Opportunities After Completing a Data Science Course in Sambhaji Nagar Data Engineer: Building and maintaining data pipelines, and working with data storage solutions and tools such as AWS Glue, Redshift, and MySQL. 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November 19, 2024 No Comments

Best Data Science Course In Solapur With Placement 100% Placement Assistance | Live Online Sessions Data Science course : 4 Month Personalized Live Advance Data Science Training is taught by industry experts in a comprehensive & question-oriented format. Enroll Before: 07 December, 2024 Download Broucher Apply now 1000+ Students Trained 100% Placement Assistance 07 December, 2024 Start Date 0% EMI Available 7:30 AM – 9:30 AM Lecture Timings ( IST ) Contact Now Key Highlights Of The Advance Data Science Course 100+ Hours Of Live Class 50+ Hours of Videos 1-on-1 Mentoring Sessions 20+ Industry Tools Mastery In-Class Live Presentations 3+ Live Projects & Practice Assignments 100% Placement Assistance Resume & Interview Training Get familiar with our online Python Data Science course syllabus. Download Broucher Syllabus for Data Science Course Online Advance Data Science Course in Solapur is designed to teach students the basics to the advanced level concepts of Python Data Science with practice assignments and offline in-class projects which helps them to get placed in MNC’s. Term 1 In this term, you will learn how to ace Python Basics, Python OOPS and Python Libraries like Pandas, Matplotlib, Numpy, etc…   Module 1 :- Python Python Basics :  Why python Python IDE Basics of programming Variables , Data Types Conditional statements Loops Logical Thinking Data Structures Functions and types of arguments Lambda Functions memory Management garbage collector Copies – shallow copy, deep copy Higher Order Functions – Map , Reduce , Filter Iterable , Iterator , generator Exception handling Programming interview questions Python OOPS : Class constructor and its types , Destructor Types of variables – instance , static Inheritance – Single , Multiple , Multilevel , Hierarchical polymorphism duck typing Overloading – method , Operator , constructor overriding – method , Constructor Super Function Encapsulation access Modifiers Abstraction monkey patching Module 2:- Python Libraries Pandas : Introduction to PandasSeries Data Structure Data Frame Data Structure Merging DataFrame Read Complex CSV , JSON , excel Files using pandas Write to File Data Frame Manipulation – head , Tail , Describe , shape ,Drop , inplace loc & I=iloc Apply Function Value count Add Column Add Row To DataFrame – using concat,Append Order By Operation Sort Values Group by operation Pivot Table Date/ Time Functionality Example Manipulating DataFrame Matplotlib : line graph bar Plot scatter plot pie chart other function Numpy : Introduction to Numpy Creating Arrays , Indexing , Slicing Data Types Copy vs View Array Shape & Reshape Arrays Split & Joins Arrays Filter Seaborn Model Data Engineering in Python : Handling Missing Data Techniques to inpute missing Values Meaningful Data transformation Encoding DataData Visualization in Python Read Json , CSV’s, excels Term 2 In this term, you will learn how to ace MySQL, AWS, Tools & IDE’S Module 3:- SQL/ETL MYSQL : DBMS & RDBMS Data Types DQL DDL DML TCL DCL Key Constraints Operators Clouses Aggregate Functions Indexes Views Triggers JOINS Sub Queries & Nested Queries   Module 4:- AWS Use of AWS Cloud computing models S3 AWS Data Pipeline EMR AWS Glue Athena Redshift Module 5:- Tools Azure devops : Use Of Devops CI/ CD Pipeline work item sprints repository state of task Repose Clone pull request Git : Use of Git feature branch clone Add Commit Push IDE : PowerBI : Dashboards Application DBeaver : Connection Process DB Manipulation Jupyter Notebook : Google Colaboratory Pycharm Term 3 In this term, you will learn how to ace Framework and industry projects Module 6:- Framework Pyspark : Use of Pyspark For Data Science Spark Session & RDD Timestamp Schema Parallelize Broadcast Variable Create DataFrame Transformations & actions Empty DataFrame Structure type and structure field Select Collect WithColumn Where & Filter Drop & Drop Duplicate orderby and sortby Groupby Joins union and union all union byname map , flatmap Sample by vs Sample Pivote maptype Aggregate Functions Windows Function Read and Write in CSV When Split collect Row number dense rank Module 7:- Final Projects and course wrap-up 1) E-Commerce 2) Banking Domain 0 + HOURS OF LIVE LEARNING 0 + Live Projects 0 + HOURS OF VIDEO LEARNING Download Detailed Data Science Course Syllabus & Trainer List Get Syllabus The Career Opportunities After Completing a Data Science Course in Solapur Data Engineer: Building and maintaining data pipelines, and working with data storage solutions and tools such as AWS Glue, Redshift, and MySQL. Quantitative Analyst: Applying quantitative techniques to financial and business data, leveraging Python for data manipulation and analysis. Data Analyst: Performing data analysis and visualization using tools like Pandas, Matplotlib, and PowerBI, and creating actionable insights from data. Cloud Data Engineer: Working with cloud services and data pipelines, including AWS services like S3, EMR, and Athena. DevOps Engineer: Implementing CI/CD pipelines and managing version control with Git, focusing on automation and integration in data engineering projects. Click here for Data Analyst Course​ Tools You’ll Master Data Science Roadmap A concise data science roadmap: 1.Learn Basics: Python, statistics, and math. 2.Data Handling: Clean and manipulate data (Pandas, NumPy). 3.Data Visualization: Use tools like Matplotlib and Seaborn. 4.Machine Learning: Master algorithms (regression, classification, clustering). 5.Big Data & Tools: Learn SQL, Spark, Hadoop. 6.Projects: Build real-world projects and models. 7.Keep Learning: Stay updated with new tools and techniques.   Mentors Shamli Python Trainer 4+ Years ITExperience4+ Years Teaching Experience Govind Chavan Project Trainer 5+ Years IT Experience5+Teaching Experience Rushikesh Deshmukh Digital Marketing 3+ Years IT Experience2+Teaching Experience Bharat Kale Manual Trainer 5+ Years IT Experience5+Teaching Experience Our Learners Work At Enroll Now and get 5% Off On Course Fees Bug Spotter Reviews https://www.youtube.com/watch?v=Kpv8ladIgIs&ab_channel=BugSpotter Introduction to Data Science Data science helps us analyze and interpret large amounts of data to solve real-world problems. 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November 19, 2024 No Comments

Best Data Science Course In Nanded With Placement 100% Placement Assistance | Live Online Sessions Data Science course : 4 Month Personalized Live Advance Data Science Training is taught by industry experts in a comprehensive & question-oriented format. Enroll Before: 07 December, 2024 Download Broucher Apply now 1000+ Students Trained 100% Placement Assistance 07 December, 2024 Start Date 0% EMI Available 7:30 AM – 9:30 AM Lecture Timings ( IST ) Contact Now Key Highlights Of The Advance Data Science Course 100+ Hours Of Live Class 50+ Hours of Videos 1-on-1 Mentoring Sessions 20+ Industry Tools Mastery In-Class Live Presentations 3+ Live Projects & Practice Assignments 100% Placement Assistance Resume & Interview Training Get familiar with our online Python Data Science course syllabus. Download Broucher Syllabus for Data Science Course Online Advance Data Science Course in Pune is designed to teach students the basics to the advanced level concepts of Python Data Science with practice assignments and offline in-class projects which helps them to get placed in MNC’s. Term 1 In this term, you will learn how to ace Python Basics, Python OOPS and Python Libraries like Pandas, Matplotlib, Numpy, etc…   Module 1 :- Python Python Basics :  Why python Python IDE Basics of programming Variables , Data Types Conditional statements Loops Logical Thinking Data Structures Functions and types of arguments Lambda Functions memory Management garbage collector Copies – shallow copy, deep copy Higher Order Functions – Map , Reduce , Filter Iterable , Iterator , generator Exception handling Programming interview questions Python OOPS : Class constructor and its types , Destructor Types of variables – instance , static Inheritance – Single , Multiple , Multilevel , Hierarchical polymorphism duck typing Overloading – method , Operator , constructor overriding – method , Constructor Super Function Encapsulation access Modifiers Abstraction monkey patching Module 2:- Python Libraries Pandas : Introduction to PandasSeries Data Structure Data Frame Data Structure Merging DataFrame Read Complex CSV , JSON , excel Files using pandas Write to File Data Frame Manipulation – head , Tail , Describe , shape ,Drop , inplace loc & I=iloc Apply Function Value count Add Column Add Row To DataFrame – using concat,Append Order By Operation Sort Values Group by operation Pivot Table Date/ Time Functionality Example Manipulating DataFrame Matplotlib : line graph bar Plot scatter plot pie chart other function Numpy : Introduction to Numpy Creating Arrays , Indexing , Slicing Data Types Copy vs View Array Shape & Reshape Arrays Split & Joins Arrays Filter Seaborn Model Data Engineering in Python : Handling Missing Data Techniques to inpute missing Values Meaningful Data transformation Encoding DataData Visualization in Python Read Json , CSV’s, excels Term 2 In this term, you will learn how to ace MySQL, AWS, Tools & IDE’S Module 3:- SQL/ETL MYSQL : DBMS & RDBMS Data Types DQL DDL DML TCL DCL Key Constraints Operators Clouses Aggregate Functions Indexes Views Triggers JOINS Sub Queries & Nested Queries   Module 4:- AWS Use of AWS Cloud computing models S3 AWS Data Pipeline EMR AWS Glue Athena Redshift Module 5:- Tools Azure devops : Use Of Devops CI/ CD Pipeline work item sprints repository state of task Repose Clone pull request Git : Use of Git feature branch clone Add Commit Push IDE : PowerBI : Dashboards Application DBeaver : Connection Process DB Manipulation Jupyter Notebook : Google Colaboratory Pycharm Term 3 In this term, you will learn how to ace Framework and industry projects Module 6:- Framework Pyspark : Use of Pyspark For Data Science Spark Session & RDD Timestamp Schema Parallelize Broadcast Variable Create DataFrame Transformations & actions Empty DataFrame Structure type and structure field Select Collect WithColumn Where & Filter Drop & Drop Duplicate orderby and sortby Groupby Joins union and union all union byname map , flatmap Sample by vs Sample Pivote maptype Aggregate Functions Windows Function Read and Write in CSV When Split collect Row number dense rank Module 7:- Final Projects and course wrap-up 1) E-Commerce 2) Banking Domain 0 + HOURS OF LIVE LEARNING 0 + Live Projects 0 + HOURS OF VIDEO LEARNING Download Detailed Data Science Course Syllabus & Trainer List Get Syllabus The Career Opportunities After Completing a Data Science Course in Nanded Data Engineer: Building and maintaining data pipelines, and working with data storage solutions and tools such as AWS Glue, Redshift, and MySQL. Quantitative Analyst: Applying quantitative techniques to financial and business data, leveraging Python for data manipulation and analysis. Data Analyst: Performing data analysis and visualization using tools like Pandas, Matplotlib, and PowerBI, and creating actionable insights from data. Cloud Data Engineer: Working with cloud services and data pipelines, including AWS services like S3, EMR, and Athena. DevOps Engineer: Implementing CI/CD pipelines and managing version control with Git, focusing on automation and integration in data engineering projects. Click here for Data Analyst Course​ Tools You’ll Master Data Science course fees Unlock your potential with Bug Spotter Software Training Institute’s Data Science course for just 30,000 Rs! Dive into an exciting journey where you’ll master Python, machine learning, data visualization, and big data tools. This comprehensive program offers hands-on training, real-world projects, and expert guidance to help you become a sought-after data science professional. Plus, enjoy career support with resume building and job placement assistance. Don’t miss the opportunity to transform your career and step into the future of technology with this affordable, high-quality course! Mentors Shamli Python Trainer 4+ Years ITExperience4+ Years Teaching Experience Govind Chavan Project Trainer 5+ Years IT Experience5+Teaching Experience Rushikesh Deshmukh Digital Marketing 3+ Years IT Experience2+Teaching Experience Bharat Kale Manual Trainer 5+ Years IT Experience5+Teaching Experience Our Learners Work At Enroll Now and get 5% Off On Course Fees Bug Spotter Reviews https://www.youtube.com/watch?v=Kpv8ladIgIs&ab_channel=BugSpotter Introduction to Data Science Data science is the process of using data to uncover patterns and insights that solve real-world problems. It helps businesses make informed decisions by analyzing large datasets, turning them into actionable knowledge. Why Data Science Matters Data is all around us—from online shopping to social media.

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Best Data Science Course In Akola

November 19, 2024 No Comments

Best Data Science Course In Akola With Placement 100% Placement Assistance | Live Online Sessions Data Science course : 4 Month Personalized Live Advance Data Science Training is taught by industry experts in a comprehensive & question-oriented format. Enroll Before: 07 December, 2024 Download Broucher Apply now 1000+ Students Trained 100% Placement Assistance 07 December, 2024 Start Date 0% EMI Available 7:30 AM – 9:30 AM Lecture Timings ( IST ) Contact Now Key Highlights Of The Advance Data Science Course 100+ Hours Of Live Class 50+ Hours of Videos 1-on-1 Mentoring Sessions 20+ Industry Tools Mastery In-Class Live Presentations 3+ Live Projects & Practice Assignments 100% Placement Assistance Resume & Interview Training Get familiar with our online Python Data Science course syllabus. Download Broucher Syllabus for Data Science Course Online Advance Data Science Course in Solapur is designed to teach students the basics to the advanced level concepts of Python Data Science with practice assignments and offline in-class projects which helps them to get placed in MNC’s. Term 1 In this term, you will learn how to ace Python Basics, Python OOPS and Python Libraries like Pandas, Matplotlib, Numpy, etc…   Module 1 :- Python Python Basics :  Why python Python IDE Basics of programming Variables , Data Types Conditional statements Loops Logical Thinking Data Structures Functions and types of arguments Lambda Functions memory Management garbage collector Copies – shallow copy, deep copy Higher Order Functions – Map , Reduce , Filter Iterable , Iterator , generator Exception handling Programming interview questions Python OOPS : Class constructor and its types , Destructor Types of variables – instance , static Inheritance – Single , Multiple , Multilevel , Hierarchical polymorphism duck typing Overloading – method , Operator , constructor overriding – method , Constructor Super Function Encapsulation access Modifiers Abstraction monkey patching Module 2:- Python Libraries Pandas : Introduction to PandasSeries Data Structure Data Frame Data Structure Merging DataFrame Read Complex CSV , JSON , excel Files using pandas Write to File Data Frame Manipulation – head , Tail , Describe , shape ,Drop , inplace loc & I=iloc Apply Function Value count Add Column Add Row To DataFrame – using concat,Append Order By Operation Sort Values Group by operation Pivot Table Date/ Time Functionality Example Manipulating DataFrame Matplotlib : line graph bar Plot scatter plot pie chart other function Numpy : Introduction to Numpy Creating Arrays , Indexing , Slicing Data Types Copy vs View Array Shape & Reshape Arrays Split & Joins Arrays Filter Seaborn Model Data Engineering in Python : Handling Missing Data Techniques to inpute missing Values Meaningful Data transformation Encoding DataData Visualization in Python Read Json , CSV’s, excels Term 2 In this term, you will learn how to ace MySQL, AWS, Tools & IDE’S Module 3:- SQL/ETL MYSQL : DBMS & RDBMS Data Types DQL DDL DML TCL DCL Key Constraints Operators Clouses Aggregate Functions Indexes Views Triggers JOINS Sub Queries & Nested Queries   Module 4:- AWS Use of AWS Cloud computing models S3 AWS Data Pipeline EMR AWS Glue Athena Redshift Module 5:- Tools Azure devops : Use Of Devops CI/ CD Pipeline work item sprints repository state of task Repose Clone pull request Git : Use of Git feature branch clone Add Commit Push IDE : PowerBI : Dashboards Application DBeaver : Connection Process DB Manipulation Jupyter Notebook : Google Colaboratory Pycharm Term 3 In this term, you will learn how to ace Framework and industry projects Module 6:- Framework Pyspark : Use of Pyspark For Data Science Spark Session & RDD Timestamp Schema Parallelize Broadcast Variable Create DataFrame Transformations & actions Empty DataFrame Structure type and structure field Select Collect WithColumn Where & Filter Drop & Drop Duplicate orderby and sortby Groupby Joins union and union all union byname map , flatmap Sample by vs Sample Pivote maptype Aggregate Functions Windows Function Read and Write in CSV When Split collect Row number dense rank Module 7:- Final Projects and course wrap-up 1) E-Commerce 2) Banking Domain 0 + HOURS OF LIVE LEARNING 0 + Live Projects 0 + HOURS OF VIDEO LEARNING Download Detailed Data Science Course Syllabus & Trainer List Get Syllabus The Career Opportunities After Completing a Data Science Course in Akola Data Engineer: Building and maintaining data pipelines, and working with data storage solutions and tools such as AWS Glue, Redshift, and MySQL. Quantitative Analyst: Applying quantitative techniques to financial and business data, leveraging Python for data manipulation and analysis. Data Analyst: Performing data analysis and visualization using tools like Pandas, Matplotlib, and PowerBI, and creating actionable insights from data. Cloud Data Engineer: Working with cloud services and data pipelines, including AWS services like S3, EMR, and Athena. DevOps Engineer: Implementing CI/CD pipelines and managing version control with Git, focusing on automation and integration in data engineering projects. Click here for Data Analyst Course​ Tools You’ll Master Why Python for Data Science Python is widely favored for data science due to its simplicity, readability, and versatility. It offers an extensive collection of powerful libraries like Pandas for data manipulation, NumPy for numerical computing, Matplotlib and Seaborn for data visualization, and Scikit-learn for machine learning. Python’s rich ecosystem of tools and frameworks makes it ideal for handling diverse data science tasks, from data cleaning and analysis to advanced machine learning and deep learning. Moreover, its strong community support and vast resources make it easier for beginners and experts alike to develop data-driven solutions efficiently. Mentors Shamli Python Trainer 4+ Years ITExperience4+ Years Teaching Experience Govind Chavan Project Trainer 5+ Years IT Experience5+Teaching Experience Rushikesh Deshmukh Digital Marketing 3+ Years IT Experience2+Teaching Experience Bharat Kale Manual Trainer 5+ Years IT Experience5+Teaching Experience Our Learners Work At Enroll Now and get 5% Off On Course Fees Bug Spotter Reviews https://www.youtube.com/watch?v=Kpv8ladIgIs&ab_channel=BugSpotter Introduction to Data Science Data science is about using data to find patterns and solve problems. It’s like being a detective who looks at information to uncover insights that help businesses make smarter choices. As technology grows, data science has become essential

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Enroll Now and get 5% Off On Course Fees