Introduction to Decision Intelligence using DataKaren Lim
This document outlines the modules in the Data for Decision Intelligence programme at Ngee Ann Polytechnic. The 4 modules are: 1) Data Wrangling and Statistics, which teaches data analysis using R and DataCamp; 2) Visualization of Data with R & Tableau, which teaches data visualization in R and Tableau; 3) Machine Learning Modelling, which covers regression, trees and other techniques; and 4) Design Thinking for Data Science, which teaches integrating human insights with machine learning and building data science projects.
The document provides an overview of the field of data science, discussing what data science entails, the roles of data analysts, engineers, and scientists, as well as the key skills involved which include programming languages like Python and R, tools like Spark and SQL, statistics, machine learning, and domain expertise. It also briefly touches on debates around tools and languages and the relationship between statistics and computer science in data science work.
This document provides an overview of parametric and non-parametric supervised machine learning. Parametric learning uses a fixed number of parameters and makes strong assumptions about the data, while non-parametric learning uses a flexible number of parameters that grows with more data, making fewer assumptions. Common examples of parametric models include linear regression and logistic regression, while non-parametric examples include K-nearest neighbors, decision trees, and neural networks. The document also briefly discusses calculating parameters using ordinary least mean square for parametric models and the limitations when data does not follow predefined assumptions.
This document discusses data analysis and visualization using ggplot2 in R. It explains that ggplot2 uses a grammar of graphics consisting of data, geometry, and aesthetics. Data is the data frame containing variables to plot, geometry defines the type of graphic like histograms or boxplots, and aesthetics indicate which variables to map to different visual properties. It provides examples of creating different plots using the mtcars dataset, including a bivariate plot of hp vs disp, a dotplot of gear, a barchart of carb, and a grid of hp vs disp plots faceted by gear. It also gives an example of a dotplot of gear for cars with over 200 cubic inches of displacement.
This document provides tips for winning data science competitions by summarizing a presentation about strategies and techniques. It discusses the structure of competitions, sources of competitive advantage like feature engineering and the right tools, and validation approaches. It also summarizes three case studies where the speaker applied these lessons, including encoding categorical variables and building diverse blended models. The key lessons are to focus on proper validation, leverage domain knowledge through features, and apply what is learned to real-world problems.
This document discusses parametric and nonparametric machine learning algorithms. Parametric algorithms use a fixed number of parameters to model data, while nonparametric algorithms make fewer assumptions about the underlying function. Parametric algorithms are simpler and faster but are limited in complexity, while nonparametric algorithms are more flexible but require more data and are slower. Examples of parametric algorithms include logistic regression and naive bayes, while k-nearest neighbors, decision trees, and support vector machines are nonparametric.
Best Python Libraries For Data Science & Machine Learning | EdurekaEdureka!
This document provides an overview of popular Python libraries for data science and machine learning tasks. It discusses libraries for statistical analysis (NumPy, SciPy, Pandas, StatsModels), data visualization (Matplotlib, Seaborn, Plotly, Bokeh), machine learning (Scikit-learn, XGBoost, Eli5), deep learning (TensorFlow, Keras, Pytorch), and natural language processing (NLTK, SpaCy, Gensim). For each category, it lists the top libraries and briefly describes their main functionalities. The document serves as an introduction to the Python data science ecosystem.
Introduction to Decision Intelligence using DataKaren Lim
This document outlines the modules in the Data for Decision Intelligence programme at Ngee Ann Polytechnic. The 4 modules are: 1) Data Wrangling and Statistics, which teaches data analysis using R and DataCamp; 2) Visualization of Data with R & Tableau, which teaches data visualization in R and Tableau; 3) Machine Learning Modelling, which covers regression, trees and other techniques; and 4) Design Thinking for Data Science, which teaches integrating human insights with machine learning and building data science projects.
The document provides an overview of the field of data science, discussing what data science entails, the roles of data analysts, engineers, and scientists, as well as the key skills involved which include programming languages like Python and R, tools like Spark and SQL, statistics, machine learning, and domain expertise. It also briefly touches on debates around tools and languages and the relationship between statistics and computer science in data science work.
This document provides an overview of parametric and non-parametric supervised machine learning. Parametric learning uses a fixed number of parameters and makes strong assumptions about the data, while non-parametric learning uses a flexible number of parameters that grows with more data, making fewer assumptions. Common examples of parametric models include linear regression and logistic regression, while non-parametric examples include K-nearest neighbors, decision trees, and neural networks. The document also briefly discusses calculating parameters using ordinary least mean square for parametric models and the limitations when data does not follow predefined assumptions.
This document discusses data analysis and visualization using ggplot2 in R. It explains that ggplot2 uses a grammar of graphics consisting of data, geometry, and aesthetics. Data is the data frame containing variables to plot, geometry defines the type of graphic like histograms or boxplots, and aesthetics indicate which variables to map to different visual properties. It provides examples of creating different plots using the mtcars dataset, including a bivariate plot of hp vs disp, a dotplot of gear, a barchart of carb, and a grid of hp vs disp plots faceted by gear. It also gives an example of a dotplot of gear for cars with over 200 cubic inches of displacement.
This document provides tips for winning data science competitions by summarizing a presentation about strategies and techniques. It discusses the structure of competitions, sources of competitive advantage like feature engineering and the right tools, and validation approaches. It also summarizes three case studies where the speaker applied these lessons, including encoding categorical variables and building diverse blended models. The key lessons are to focus on proper validation, leverage domain knowledge through features, and apply what is learned to real-world problems.
This document discusses parametric and nonparametric machine learning algorithms. Parametric algorithms use a fixed number of parameters to model data, while nonparametric algorithms make fewer assumptions about the underlying function. Parametric algorithms are simpler and faster but are limited in complexity, while nonparametric algorithms are more flexible but require more data and are slower. Examples of parametric algorithms include logistic regression and naive bayes, while k-nearest neighbors, decision trees, and support vector machines are nonparametric.
Best Python Libraries For Data Science & Machine Learning | EdurekaEdureka!
This document provides an overview of popular Python libraries for data science and machine learning tasks. It discusses libraries for statistical analysis (NumPy, SciPy, Pandas, StatsModels), data visualization (Matplotlib, Seaborn, Plotly, Bokeh), machine learning (Scikit-learn, XGBoost, Eli5), deep learning (TensorFlow, Keras, Pytorch), and natural language processing (NLTK, SpaCy, Gensim). For each category, it lists the top libraries and briefly describes their main functionalities. The document serves as an introduction to the Python data science ecosystem.
An introduction to R is a document usefulssuser3c3f88
R is a language and environment for statistical computing and graphics. It provides functions for data manipulation, calculation, and graphical displays. Key features of R include its ability to produce publication-quality plots, perform statistical tests, fit models to data, and develop statistical software. R has an extensive library of additional user-contributed packages that extend its capabilities. The document provides information on downloading and using R, reading data into R, customizing plots, and interactive plotting functions.
Data pipelines are the heart and soul of data science. Are you a beginner looking to understand data pipelines? A glimpse into what they are and how they work.
Data Science - Part II - Working with R & R studioDerek Kane
This tutorial will go through a basic primer for individuals who want to get started with predictive analytics through downloading the open source (FREE) language R. I will go through some tips to get up and started and building predictive models ASAP.
This document provides an overview of data science tools, techniques, and applications. It begins by defining data science and explaining why it is an important and in-demand field. Examples of applications in healthcare, marketing, and logistics are given. Common computational tools for data science like RapidMiner, WEKA, R, Python, and Rattle are described. Techniques like regression, classification, clustering, recommendation, association rules, outlier detection, and prediction are explained along with examples of how they are used. The advantages of using computational tools to analyze data are highlighted.
Basic of R Programming Language,
Introduction, How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes
Basic of R Programming Language
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
R is a popular programming language for statistical analysis and visualization. It allows users to import, clean, analyze, and visualize data, and is commonly used in fields like data science, machine learning, and research. The document provides an overview of R, including how to download and install it, basic usage like starting an R session and running commands, and examples of using R for tasks like data analysis, statistical computing, and machine learning. Key features of R highlighted are that it is open source, runs on various platforms, and has a large collection of packages for data handling and analysis.
To succeed as a data scientist, you should follow a structured path known as the “Data Science Roadmap.” This path outlines foundational knowledge in math and programming. Data manipulation and visualization, exploratory data analysis. Machine learning, deep learning, and advanced topics such as natural language processing and time series analysis. Following this roadmap can help you acquire the skills and knowledge needed to excel in this rapidly growing field.
Becoming a successful data scientist requires a unique combination of technical skills, business acumen, and critical thinking ability. To achieve your career goals in this field, you need a structured plan or a data science roadmap that outlines the skills, tools, and knowledge required to succeed. In this blog, we’ll take a closer look at what a data science roadmap is, why it’s important, and how to create one that works for you.
At its core, It is a structured plan that outlines the skills, tools, and knowledge required to become a successful data scientist. It serves as a guidepost to help individuals navigate the complex landscape of data science and provides a clear path towards achieving their career objectives.
Data Science Job ready #DataScienceInterview Question and Answers 2022 | #Dat...Rohit Dubey
How Much Do Data Scientists Make?
The demand and salary for data scientists tend to be higher than most other ITES jobs. Experience is one of the key factors in determining the salary range of a data science professional.
According to Glassdoor, a Data Scientist in the United States earns an annual average of USD 117,212, and the same site reports that Data Scientists in India make a yearly average of ₹1,000,000.
Data Scientist Career Path
Data Science is currently considered one of the most lucrative careers available. Companies across all major industries/sectors have data scientist requirements to help them gain valuable insights from big data. There is a sharp growth in demand for highly skilled data science professionals who can straddle the business and IT worlds.
The career path to becoming a data scientist isn’t clearly defined since this is a relatively new profession. People from different backgrounds like mathematics, statistics, computer science or economics, end up in data science.
The major designations for data science professionals are:
Data Analyst
Data Scientist (entry-level)
Associate data scientist
Data Scientist (senior-level)
Product Manager
Lead data scientist
Director/VP/SVP
That was all about Data Scientist Job Description.
Become a Data Scientist Today!
In this write-up, we covered the Data Scientist job description in detail. Irrespective of which location you are in, there is no dearth of jobs for skillful data scientists. A career in data science is a rewarding journey to embark on, especially in the finance, retail, and e-commerce sectors. Jobs are also available with Government departments, universities and research institutes, telecoms, transports, the list goes on.
This video covers
Introductory Questions
Data Science Introduction
Data Science Technical Interview QnA :
#Excel
#SQL
#Python3
#MachineLearning
#DataAnalyticstechnical Interview
#DataScienceProjects
#coder #statistics #datamining #dataanalyst #code #engineering #linux #codinglife #cloudcomputing #businessintelligence #robotics #softwaredeveloper #automation #cloud #neuralnetworks #sql #science #softwareengineer #digitaltransformation #computer #daysofcode #coders #bigdataanalytics #programminglife #dataviz #html #digitalmarketing #devops #datasciencetraining #dataprotection
#rohitdubey
#teachtechtoe
#datascience #datasciencetraining #datasciencejobs #datasciencecourse #datasciencenigeria #datasciencebootcamp #datascienceworkshop #datasciencecareers #datasciencestudent #datascienceproject #datascienceforall #datasciencetraininginpatelnagar#datasciencetrainingindelhi
R was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand to teach introductory statistics. It is an open source software environment excellent for data analysis and graphics using functions in an interpreter. R is used across many industries and can analyze both structured and unstructured data to explore datasets and build predictive models.
This document provides an overview of the key concepts in the syllabus for a course on data science and big data. It covers 5 units: 1) an introduction to data science and big data, 2) descriptive analytics using statistics, 3) predictive modeling and machine learning, 4) data analytical frameworks, and 5) data science using Python. Key topics include data types, analytics classifications, statistical analysis techniques, predictive models, Hadoop, NoSQL databases, and Python packages for data science. The goal is to equip students with the skills to work with large and diverse datasets using various data science tools and techniques.
This document discusses using machine learning algorithms to predict employee attrition and understand factors that influence turnover. It evaluates different machine learning models on an employee turnover dataset to classify employees who are at risk of leaving. Logistic regression and random forest classifiers are applied and achieve accuracy rates of 78% and 98% respectively. The document also discusses preprocessing techniques and visualizing insights from the models to better understand employee turnover.
R can perform various data analysis and data science tasks for free through its extensive packages and community support. It is an open-source statistical programming language that is widely used for data manipulation, visualization, and machine learning. Some key features of R include its ability to perform interactive visualization, ensemble learning, text/social media mining, and integration with other languages and technologies like SQL, Python, and Tableau. While powerful, R does have some limitations like a steep learning curve and slower execution compared to other languages.
This document provides an introduction to R, including:
- R is a software environment for data manipulation, statistical computing, and graphical data analysis. It is widely used in academia, healthcare, finance, and by large companies.
- R has two originators from New Zealand and Canada. It is developed by the R Core Team and has over 13,000 contributed packages.
- Examples of how companies like Google, Facebook, banks, John Deere, the New York Times, and Ford use R for tasks like data analysis, visualization, forecasting, and statistical modeling.
GNU R in Clinical Research and Evidence-Based MedicineAdrian Olszewski
Is GNU R (an environment for statistical computing) suitable enough for Biostatisticians involved in Clinical Research? Can it replace or support SAS in this area? Well, I think this presentation may help to remove any doubts. If you are a Biostatistician (and probably a SAS user), you may find it useful.
The presentation is under constant improvement.
You can find it also on CRAN (contributed documentation) and at http://www.r-clinical-research.com
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Secure your career by getting the Rock Rating. Our Job Assurance Program is surely the way to go if you have a job you want to land. Take the JAP and get the Rock Rating!
More Related Content
Similar to Fresher's guide to Preparing for a Big Data Interview
An introduction to R is a document usefulssuser3c3f88
R is a language and environment for statistical computing and graphics. It provides functions for data manipulation, calculation, and graphical displays. Key features of R include its ability to produce publication-quality plots, perform statistical tests, fit models to data, and develop statistical software. R has an extensive library of additional user-contributed packages that extend its capabilities. The document provides information on downloading and using R, reading data into R, customizing plots, and interactive plotting functions.
Data pipelines are the heart and soul of data science. Are you a beginner looking to understand data pipelines? A glimpse into what they are and how they work.
Data Science - Part II - Working with R & R studioDerek Kane
This tutorial will go through a basic primer for individuals who want to get started with predictive analytics through downloading the open source (FREE) language R. I will go through some tips to get up and started and building predictive models ASAP.
This document provides an overview of data science tools, techniques, and applications. It begins by defining data science and explaining why it is an important and in-demand field. Examples of applications in healthcare, marketing, and logistics are given. Common computational tools for data science like RapidMiner, WEKA, R, Python, and Rattle are described. Techniques like regression, classification, clustering, recommendation, association rules, outlier detection, and prediction are explained along with examples of how they are used. The advantages of using computational tools to analyze data are highlighted.
Basic of R Programming Language,
Introduction, How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes
Basic of R Programming Language
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
R is a popular programming language for statistical analysis and visualization. It allows users to import, clean, analyze, and visualize data, and is commonly used in fields like data science, machine learning, and research. The document provides an overview of R, including how to download and install it, basic usage like starting an R session and running commands, and examples of using R for tasks like data analysis, statistical computing, and machine learning. Key features of R highlighted are that it is open source, runs on various platforms, and has a large collection of packages for data handling and analysis.
To succeed as a data scientist, you should follow a structured path known as the “Data Science Roadmap.” This path outlines foundational knowledge in math and programming. Data manipulation and visualization, exploratory data analysis. Machine learning, deep learning, and advanced topics such as natural language processing and time series analysis. Following this roadmap can help you acquire the skills and knowledge needed to excel in this rapidly growing field.
Becoming a successful data scientist requires a unique combination of technical skills, business acumen, and critical thinking ability. To achieve your career goals in this field, you need a structured plan or a data science roadmap that outlines the skills, tools, and knowledge required to succeed. In this blog, we’ll take a closer look at what a data science roadmap is, why it’s important, and how to create one that works for you.
At its core, It is a structured plan that outlines the skills, tools, and knowledge required to become a successful data scientist. It serves as a guidepost to help individuals navigate the complex landscape of data science and provides a clear path towards achieving their career objectives.
Data Science Job ready #DataScienceInterview Question and Answers 2022 | #Dat...Rohit Dubey
How Much Do Data Scientists Make?
The demand and salary for data scientists tend to be higher than most other ITES jobs. Experience is one of the key factors in determining the salary range of a data science professional.
According to Glassdoor, a Data Scientist in the United States earns an annual average of USD 117,212, and the same site reports that Data Scientists in India make a yearly average of ₹1,000,000.
Data Scientist Career Path
Data Science is currently considered one of the most lucrative careers available. Companies across all major industries/sectors have data scientist requirements to help them gain valuable insights from big data. There is a sharp growth in demand for highly skilled data science professionals who can straddle the business and IT worlds.
The career path to becoming a data scientist isn’t clearly defined since this is a relatively new profession. People from different backgrounds like mathematics, statistics, computer science or economics, end up in data science.
The major designations for data science professionals are:
Data Analyst
Data Scientist (entry-level)
Associate data scientist
Data Scientist (senior-level)
Product Manager
Lead data scientist
Director/VP/SVP
That was all about Data Scientist Job Description.
Become a Data Scientist Today!
In this write-up, we covered the Data Scientist job description in detail. Irrespective of which location you are in, there is no dearth of jobs for skillful data scientists. A career in data science is a rewarding journey to embark on, especially in the finance, retail, and e-commerce sectors. Jobs are also available with Government departments, universities and research institutes, telecoms, transports, the list goes on.
This video covers
Introductory Questions
Data Science Introduction
Data Science Technical Interview QnA :
#Excel
#SQL
#Python3
#MachineLearning
#DataAnalyticstechnical Interview
#DataScienceProjects
#coder #statistics #datamining #dataanalyst #code #engineering #linux #codinglife #cloudcomputing #businessintelligence #robotics #softwaredeveloper #automation #cloud #neuralnetworks #sql #science #softwareengineer #digitaltransformation #computer #daysofcode #coders #bigdataanalytics #programminglife #dataviz #html #digitalmarketing #devops #datasciencetraining #dataprotection
#rohitdubey
#teachtechtoe
#datascience #datasciencetraining #datasciencejobs #datasciencecourse #datasciencenigeria #datasciencebootcamp #datascienceworkshop #datasciencecareers #datasciencestudent #datascienceproject #datascienceforall #datasciencetraininginpatelnagar#datasciencetrainingindelhi
R was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand to teach introductory statistics. It is an open source software environment excellent for data analysis and graphics using functions in an interpreter. R is used across many industries and can analyze both structured and unstructured data to explore datasets and build predictive models.
This document provides an overview of the key concepts in the syllabus for a course on data science and big data. It covers 5 units: 1) an introduction to data science and big data, 2) descriptive analytics using statistics, 3) predictive modeling and machine learning, 4) data analytical frameworks, and 5) data science using Python. Key topics include data types, analytics classifications, statistical analysis techniques, predictive models, Hadoop, NoSQL databases, and Python packages for data science. The goal is to equip students with the skills to work with large and diverse datasets using various data science tools and techniques.
This document discusses using machine learning algorithms to predict employee attrition and understand factors that influence turnover. It evaluates different machine learning models on an employee turnover dataset to classify employees who are at risk of leaving. Logistic regression and random forest classifiers are applied and achieve accuracy rates of 78% and 98% respectively. The document also discusses preprocessing techniques and visualizing insights from the models to better understand employee turnover.
R can perform various data analysis and data science tasks for free through its extensive packages and community support. It is an open-source statistical programming language that is widely used for data manipulation, visualization, and machine learning. Some key features of R include its ability to perform interactive visualization, ensemble learning, text/social media mining, and integration with other languages and technologies like SQL, Python, and Tableau. While powerful, R does have some limitations like a steep learning curve and slower execution compared to other languages.
This document provides an introduction to R, including:
- R is a software environment for data manipulation, statistical computing, and graphical data analysis. It is widely used in academia, healthcare, finance, and by large companies.
- R has two originators from New Zealand and Canada. It is developed by the R Core Team and has over 13,000 contributed packages.
- Examples of how companies like Google, Facebook, banks, John Deere, the New York Times, and Ford use R for tasks like data analysis, visualization, forecasting, and statistical modeling.
GNU R in Clinical Research and Evidence-Based MedicineAdrian Olszewski
Is GNU R (an environment for statistical computing) suitable enough for Biostatisticians involved in Clinical Research? Can it replace or support SAS in this area? Well, I think this presentation may help to remove any doubts. If you are a Biostatistician (and probably a SAS user), you may find it useful.
The presentation is under constant improvement.
You can find it also on CRAN (contributed documentation) and at http://www.r-clinical-research.com
Similar to Fresher's guide to Preparing for a Big Data Interview (20)
Secure your career with Rock Interview by your sideRock Interview
Secure your career by getting the Rock Rating. Our Job Assurance Program is surely the way to go if you have a job you want to land. Take the JAP and get the Rock Rating!
Rock Interview offers mentorship programs and online courses to help individuals upskill or reskill their careers during uncertain economic times. Their programs provide personalized mentor guidance and assessments to understand skill gaps and tailor training, using approaches like drip learning and interactive learning modules. The goal is to land students their dream jobs through skills training, resume and video profile building, and an affordable, accessible solution during the COVID-19 pandemic job market downturn.
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We recently hosted the much-anticipated Community Skill Builders Workshop during our June online meeting. This event was a culmination of six months of listening to your feedback and crafting solutions to better support your PMI journey. Here’s a look back at what happened and the exciting developments that emerged from our collaborative efforts.
A Gathering of Minds
We were thrilled to see a diverse group of attendees, including local certified PMI trainers and both new and experienced members eager to contribute their perspectives. The workshop was structured into three dynamic discussion sessions, each led by our dedicated membership advocates.
Key Takeaways and Future Directions
The insights and feedback gathered from these discussions were invaluable. Here are some of the key takeaways and the steps we are taking to address them:
• Enhanced Resource Accessibility: We are working on a new, user-friendly resource page that will make it easier for members to access training materials and real-world application guides.
• Structured Mentorship Program: Plans are underway to launch a mentorship program that will connect members with experienced professionals for guidance and support.
• Increased Networking Opportunities: Expect to see more frequent and varied networking events, both virtual and in-person, to help you build connections and foster a sense of community.
Moving Forward
We are committed to turning your feedback into actionable solutions that enhance your PMI journey. This workshop was just the beginning. By actively participating and sharing your experiences, you have helped shape the future of our Chapter’s offerings.
Thank you to everyone who attended and contributed to the success of the Community Skill Builders Workshop. Your engagement and enthusiasm are what make our Chapter strong and vibrant. Stay tuned for updates on the new initiatives and opportunities to get involved. Together, we are building a community that supports and empowers each other on our PMI journeys.
Stay connected, stay engaged, and let’s continue to grow together!
About PMI Silver Spring Chapter
We are a branch of the Project Management Institute. We offer a platform for project management professionals in Silver Spring, MD, and the DC/Baltimore metro area. Monthly meetings facilitate networking, knowledge sharing, and professional development. For more, visit pmissc.org.
In the intricate tapestry of life, connections serve as the vibrant threads that weave together opportunities, experiences, and growth. Whether in personal or professional spheres, the ability to forge meaningful connections opens doors to a multitude of possibilities, propelling individuals toward success and fulfillment.
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3. 1. BASIC PROGRAMMING LANGUAGES
YOU SHOULD KNOW:
At least one statistical programming language,
like R or Python (along with Numpy and Pandas Libraries)
And one database querying language like SQL
rockinterview.in
4. 2. STATISTICS:
Statistics is important to crunch data and to pick out the most important
figures out of a huge dataset. This is critical in the decision-making process
and to design experiments.
Here are a few phrases you should definitely be able to explain:
null hypothesis
P-value
maximum likelihood estimators
confidence intervals
rockinterview.in
5. 3. MACHINE LEARNING:
Familiarise yourself with how data science is used in practical
manners.
You should be able to explain K-nearest neighbours, random
forests, and ensemble methods.
These techniques are typically implemented in R or Python.
rockinterview.in
6. 4. DATA WRANGLING:
You should be able to identify corrupt or impure data and
correct them.
This basically means understanding that a negative number
cannot exist in a dataset that describes population, or a grey
and gray are the same colour, etc…
rockinterview.in
7. 5. DATA VISUALISATION:
Learn to use data visualisation tools like ggplot, as they help you
present data and findings in a cohesive manner.
This is an important skill set, as it ensures that Product Managers and
other stakeholders understand your work and incorporate it in the
product.
.
rockinterview.in
8. 6. SOFTWARE ENGINEERING:
Know the use cases and run time of these data structures:
Queues, Arrays, Lists, Stacks, Trees, etc.
These are often necessary in creating efficient algorithms for
machine learning.
rockinterview.in
9. 6. SOFTWARE ENGINEERING:
Know the use cases and run time of these data structures:
Queues, Arrays, Lists, Stacks, Trees, etc.
These are often necessary in creating efficient algorithms for
machine learning.
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10. 7. PRODUCT MANAGEMENT:
Data Scientists that understand the product are the ones who will know
what metrics are the most important.
Usability Testing
Wireframing
Retention
Conversion Rates
Traffic Analysis
Know what these terms mean:
Customer Feedback
Internal Logs
A/B Testing
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11. Take a mock interview with us to find out more.
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