Congratulations 🎉 on taking the first step towards your Tech Dream Career!
Explore career paths in different types of companies and how to get there
High Growth Startups
Next best step You can immediately work towards this path, its your most achievable next move
Focus on past work insights & qualifications
Tests SQL and Excel skills with 1-2 critical thinking questions
Write SQL queries and Python Scripts
Alternatively, you might be asked to write executable codes & demo for a detailed Problem statement
Business case questions, Statistical analysis and Product metrics
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
1. Collecting and cleaning data from various sources: High growth startups often have limited resources and data analysts may be responsible for collecting and cleaning data from multiple sources
2. Analyzing data to identify trends and patterns: Data analysts at startups use statistical techniques and tools to identify trends and patterns in the data that can inform business decisions
3. Creating visualizations and reports: Data analysts create visualizations and reports to communicate their findings to stakeholders and help inform decision-making
4. Identifying growth opportunities: Data analysts work with the rest of the team to identify growth opportunities and develop strategies to capitalize on them
5. Building data infrastructure: Data analysts at startups may also be responsible for building and maintaining the data infrastructure that the company uses
6. Helping to scale the company: As startups grow rapidly, data analysts play a key role in scaling the company's operations and support the teams in making data-driven decisions
7. Collaboration: Data analysts at startups often work closely with other teams such as product, marketing and engineering to identify and solve problems and also to understand the product or service better
Work hours are demanding, work weeks might stretch to 60-65 hours. During production issues, or release deadlines, overtime or weekend can be expected
Usually proper reporting structure is absent, often engineers are reporting to Product or Business leaders in absence of engineering managers
Develop statistical methods or machine learning models to solve a business problem by extracting, creating and maintaining complex data sets.
Leave policies are usually not very defined for tech teams. Can intimate on Slack/WhatsApp for leaves
To begin, register on our website here. Following registration, you'll need to pay the exam fee of ₹1200 for NSAT - our online qualifying entrance test. After completing the test, those who qualify will proceed to the personal interview and group discussion round.
Gain problem solving intuition and understand how scalable products are built
Every class is followed by challenging assignments & a project module of your choice, that keeps you on your toes.
Our beginner, intermediate and advanced tracks make sure that every Newton School graduate is ready for industry
Industry-tested curriculum, taught by veterans with experience at top product companies!
Focus on past work insights & qualifications
Tests SQL and Excel skills with 1-2 critical thinking questions
Write SQL queries and Python Scripts
Alternatively, you might be asked to write executable codes & demo for a detailed Problem statement
Business case questions, Statistical analysis and Product metrics
To assess your fitment to the company dependent on your experience, interests and qualities
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
1. Collecting and cleaning data from various sources: High growth startups often have limited resources and data analysts may be responsible for collecting and cleaning data from multiple sources
2. Analyzing data to identify trends and patterns: Data analysts at startups use statistical techniques and tools to identify trends and patterns in the data that can inform business decisions
3. Creating visualizations and reports: Data analysts create visualizations and reports to communicate their findings to stakeholders and help inform decision-making
4. Identifying growth opportunities: Data analysts work with the rest of the team to identify growth opportunities and develop strategies to capitalize on them
5. Building data infrastructure: Data analysts at startups may also be responsible for building and maintaining the data infrastructure that the company uses
6. Helping to scale the company: As startups grow rapidly, data analysts play a key role in scaling the company's operations and support the teams in making data-driven decisions
7. Collaboration: Data analysts at startups often work closely with other teams such as product, marketing and engineering to identify and solve problems and also to understand the product or service better
Work hours are demanding, work weeks might stretch to 60-65 hours. During production issues, or release deadlines, overtime or weekend can be expected
Usually proper reporting structure is absent, often engineers are reporting to Product or Business leaders in absence of engineering managers
Develop statistical methods or machine learning models to solve a business problem by extracting, creating and maintaining complex data sets.
Leave policies are usually not very defined for tech teams. Can intimate on Slack/WhatsApp for leaves
Focus on past work insights & qualifications
In-depth discussion on solving a real-world machine learning problem, ask right questions, Data science concepts like supervised learning, unsupervised learning, deep learning etc.
Discussion on mathematical fundamentals, technical domain expertise and presentation of any modifications during the discussion
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
Focus on past work insights & qualifications
In-depth discussion on solving a real-world machine learning problem, ask right questions, Data science concepts like supervised learning, unsupervised learning, deep learning etc.
Discussion on mathematical fundamentals, technical domain expertise and presentation of any modifications during the discussion
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
Scaled Startups
Next best step You can immediately work towards this path, its your most achievable next move
Focus on past work insights & qualifications
Tests SQL and Excel skills with 1-2 critical thinking questions
Write SQL queries and Python Scripts
Alternatively, you might be asked to write executable codes & demo for a detailed Problem statement
Business case questions, Statistical analysis and Product metrics
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
1. Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards that monitor and drive key business decisions.
2. Embed analytics into day-to-day operations by supporting and translating business inquiries to analytical reports
3. Build robust operational and business metrics and make them highly visual and consumable across the workplace.
4. Work with cross-functional teams, systems, and vendor data to build reporting systems and utilize metrics to find strong improvement opportunities.
Work hours are demanding, work weeks might stretch to 60-65 hours. During production issues, or release deadlines, overtime or weekend can be expected
Usually proper reporting structure is absent, often engineers are reporting to Product or Business leaders in absence of engineering managers
Develop statistical methods or machine learning models to solve a business problem by extracting, creating and maintaining complex data sets.
Leave policies are usually not very defined for tech teams. Can intimate on Slack/WhatsApp for leaves
To begin, register on our website here. Following registration, you'll need to pay the exam fee of ₹1200 for NSAT - our online qualifying entrance test. After completing the test, those who qualify will proceed to the personal interview and group discussion round.
Gain problem solving intuition and understand how scalable products are built
Every class is followed by challenging assignments & a project module of your choice, that keeps you on your toes.
Our beginner, intermediate and advanced tracks make sure that every Newton School graduate is ready for industry
Industry-tested curriculum, taught by veterans with experience at top product companies!
Focus on past work insights & qualifications
Tests SQL and Excel skills with 1-2 critical thinking questions
Write SQL queries and Python Scripts
Alternatively, you might be asked to write executable codes & demo for a detailed Problem statement
Business case questions, Statistical analysis and Product metrics
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
1. Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards that monitor and drive key business decisions.
2. Embed analytics into day-to-day operations by supporting and translating business inquiries to analytical reports
3. Build robust operational and business metrics and make them highly visual and consumable across the workplace.
4. Work with cross-functional teams, systems, and vendor data to build reporting systems and utilize metrics to find strong improvement opportunities.
Work hours are demanding, work weeks might stretch to 60-65 hours. During production issues, or release deadlines, overtime or weekend can be expected
Usually proper reporting structure is absent, often engineers are reporting to Product or Business leaders in absence of engineering managers
Develop statistical methods or machine learning models to solve a business problem by extracting, creating and maintaining complex data sets.
Leave policies are usually not very defined for tech teams. Can intimate on Slack/WhatsApp for leaves
Focus on past work insights & qualifications
In-depth discussion on solving a real-world machine learning problem, ask right questions, Data science concepts like supervised learning, unsupervised learning, deep learning etc.
Discussion on mathematical fundamentals, technical domain expertise and presentation of any modifications during the discussion
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
Focus on past work insights & qualifications
In-depth discussion on solving a real-world machine learning problem, ask right questions, Data science concepts like supervised learning, unsupervised learning, deep learning etc.
Discussion on mathematical fundamentals, technical domain expertise and presentation of any modifications during the discussion
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
Big Tech Companies
Later career opportunity Sets you up for any other data role. You'd need significant mastery in data analytics concepts with an exceptional work record
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Focus on past work insights & qualifications
Tests SQL and Excel skills with 1-2 critical thinking questions
Emphasis on projects built in the past and tech stack relevance to the company
Alternatively, you might be asked to write executable codes & demo for a detailed Problem statement
SQL queries and ETLs, Business case questions, Statistical analysis and Product metrics
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
1. Translate complex or ambiguous business problem statements into analysis requirements
2. Define analytical approach; review and vet analytical approach with stakeholders
3. Solve ambiguous analyses with less well-defined inputs and outputs; drive to the heart of the problem and identify root causes
4. Identify and implement optimal communication mechanisms based on the data set and the stakeholders involved
Good work-life balance, usually not more than 35-40 hours per week. Typically no interruptions post work hours or when on leave
Traditional reporting manager structure
Develop statistical methods or machine learning models to solve a business problem by extracting, creating and maintaining complex data sets.
Standard leave policies with fixed number of leaves available
To begin, register on our website here. Following registration, you'll need to pay the exam fee of ₹1200 for NSAT - our online qualifying entrance test. After completing the test, those who qualify will proceed to the personal interview and group discussion round.
Gain problem solving intuition and understand how scalable products are built
Every class is followed by challenging assignments & a project module of your choice, that keeps you on your toes.
Our beginner, intermediate and advanced tracks make sure that every Newton School graduate is ready for industry
Industry-tested curriculum, taught by veterans with experience at top product companies!
Focus on past work insights & qualifications
Tests SQL and Excel skills with 1-2 critical thinking questions
Emphasis on projects built in the past and tech stack relevance to the company
Alternatively, you might be asked to write executable codes & demo for a detailed Problem statement
SQL queries and ETLs, Business case questions, Statistical analysis and Product metrics
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
1. Translate complex or ambiguous business problem statements into analysis requirements
2. Define analytical approach; review and vet analytical approach with stakeholders
3. Solve ambiguous analyses with less well-defined inputs and outputs; drive to the heart of the problem and identify root causes
4. Identify and implement optimal communication mechanisms based on the data set and the stakeholders involved
Good work-life balance, usually not more than 35-40 hours per week. Typically no interruptions post work hours or when on leave
Traditional reporting manager structure
Develop statistical methods or machine learning models to solve a business problem by extracting, creating and maintaining complex data sets.
Standard leave policies with fixed number of leaves available
Focus on past work insights & qualifications
In-depth discussion on solving a real-world machine learning problem, ask right questions, Data science concepts like supervised learning, unsupervised learning, deep learning etc.
Discussion on mathematical fundamentals, technical domain expertise and presentation of any modifications during the discussion
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
Focus on past work insights & qualifications
In-depth discussion on solving a real-world machine learning problem, ask right questions, Data science concepts like supervised learning, unsupervised learning, deep learning etc.
Discussion on mathematical fundamentals, technical domain expertise and presentation of any modifications during the discussion
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
Product MNCs
Stretched target You might need longer time, higher level of conceptual understanding of data analyics with relevant work experience
Focus on past work insights & qualifications
Tests SQL and Excel skills with 1-2 critical thinking questions
Emphasis on projects built in the past and tech stack relevance to the company
Alternatively, you might be asked to write executable codes & demo for a detailed Problem statement
SQL queries and ETLs, Business case questions, Statistical analysis and Product metrics
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
1. Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards that monitor and drive key business decisions.
2. Embed analytics into day-to-day operations by supporting and translating business inquiries to analytical reports
3. Build robust operational and business metrics and make them highly visual and consumable across the workplace.
4. Work with cross-functional teams, systems, and vendor data to build reporting systems and utilize metrics to find strong improvement opportunities.
Good work-life balance, usually not more than 35-40 hours per week. Typically no interruptions post work hours or when on leave
Traditional reporting manager structure
Develop statistical methods or machine learning models to solve a business problem by extracting, creating and maintaining complex data sets.
Standard leave policies with fixed number of leaves available
To begin, register on our website here. Following registration, you'll need to pay the exam fee of ₹1200 for NSAT - our online qualifying entrance test. After completing the test, those who qualify will proceed to the personal interview and group discussion round.
Gain problem solving intuition and understand how scalable products are built
Every class is followed by challenging assignments & a project module of your choice, that keeps you on your toes.
Our beginner, intermediate and advanced tracks make sure that every Newton School graduate is ready for industry
Industry-tested curriculum, taught by veterans with experience at top product companies!
Focus on past work insights & qualifications
Tests SQL and Excel skills with 1-2 critical thinking questions
Emphasis on projects built in the past and tech stack relevance to the company
Alternatively, you might be asked to write executable codes & demo for a detailed Problem statement
SQL queries and ETLs, Business case questions, Statistical analysis and Product metrics
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
1. Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards that monitor and drive key business decisions.
2. Embed analytics into day-to-day operations by supporting and translating business inquiries to analytical reports
3. Build robust operational and business metrics and make them highly visual and consumable across the workplace.
4. Work with cross-functional teams, systems, and vendor data to build reporting systems and utilize metrics to find strong improvement opportunities.
Good work-life balance, usually not more than 35-40 hours per week. Typically no interruptions post work hours or when on leave
Traditional reporting manager structure
Develop statistical methods or machine learning models to solve a business problem by extracting, creating and maintaining complex data sets.
Standard leave policies with fixed number of leaves available
Focus on past work insights & qualifications
In-depth discussion on solving a real-world machine learning problem, ask right questions, Data science concepts like supervised learning, unsupervised learning, deep learning etc.
Discussion on mathematical fundamentals, technical domain expertise and presentation of any modifications during the discussion
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
Focus on past work insights & qualifications
In-depth discussion on solving a real-world machine learning problem, ask right questions, Data science concepts like supervised learning, unsupervised learning, deep learning etc.
Discussion on mathematical fundamentals, technical domain expertise and presentation of any modifications during the discussion
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
Dream Companies
Later career opportunity Sets you up for any other data role. You'd need significant mastery in data analytics concepts with an exceptional work record
Focus on past work insights & qualifications
Tests SQL and Excel skills with 1-2 critical thinking questions
Emphasis on projects built in the past and tech stack relevance to the company
Alternatively, you might be asked to write executable codes & demo for a detailed Problem statement
SQL queries and ETLs, Business case questions, Statistical analysis and Product metrics
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
1. Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards that monitor and drive key business decisions.
2. Embed analytics into day-to-day operations by supporting and translating business inquiries to analytical reports
3. Build robust operational and business metrics and make them highly visual and consumable across the workplace.
4. Work with cross-functional teams, systems, and vendor data to build reporting systems and utilize metrics to find strong improvement opportunities.
Good work-life balance, usually not more than 35-40 hours per week. Typically no interruptions post work hours or when on leave
Traditional reporting manager structure
Develop statistical methods or machine learning models to solve a business problem by extracting, creating and maintaining complex data sets.
Standard leave policies with fixed number of leaves available
To begin, register on our website here. Following registration, you'll need to pay the exam fee of ₹1200 for NSAT - our online qualifying entrance test. After completing the test, those who qualify will proceed to the personal interview and group discussion round.
Gain problem solving intuition and understand how scalable products are built
Every class is followed by challenging assignments & a project module of your choice, that keeps you on your toes.
Our beginner, intermediate and advanced tracks make sure that every Newton School graduate is ready for industry
Industry-tested curriculum, taught by veterans with experience at top product companies!
Focus on past work insights & qualifications
Tests SQL and Excel skills with 1-2 critical thinking questions
Emphasis on projects built in the past and tech stack relevance to the company
Alternatively, you might be asked to write executable codes & demo for a detailed Problem statement
SQL queries and ETLs, Business case questions, Statistical analysis and Product metrics
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
1. Own the design, development, and maintenance of ongoing metrics, reports, analyses, and dashboards that monitor and drive key business decisions.
2. Embed analytics into day-to-day operations by supporting and translating business inquiries to analytical reports
3. Build robust operational and business metrics and make them highly visual and consumable across the workplace.
4. Work with cross-functional teams, systems, and vendor data to build reporting systems and utilize metrics to find strong improvement opportunities.
Good work-life balance, usually not more than 35-40 hours per week. Typically no interruptions post work hours or when on leave
Very traditional reporting structures, everyone has reporting managers
Develop statistical methods or machine learning models to solve a business problem by extracting, creating and maintaining complex data sets.
Company-wide mass-leaves, team outings, corporate holidays are common. Best employee benefits and perks in the industry
Focus on past work insights & qualifications
In-depth discussion on solving a real-world machine learning problem, ask right questions, Data science concepts like supervised learning, unsupervised learning, deep learning etc.
Discussion on mathematical fundamentals, technical domain expertise and presentation of any modifications during the discussion
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
Focus on past work insights & qualifications
In-depth discussion on solving a real-world machine learning problem, ask right questions, Data science concepts like supervised learning, unsupervised learning, deep learning etc.
Discussion on mathematical fundamentals, technical domain expertise and presentation of any modifications during the discussion
To assess your fitment to the company dependent on your experience, interests and qualities.
The interviewer will make an attempt to understand your aspirations, strengths, weaknesses and basically all about you
Gain problem solving intuition and understand how scalable products are built
Every class is followed by challenging assignments & a project module of your choice, that keeps you on your toes.
Our beginner, intermediate and advanced tracks make sure that every Newton School graduate is ready for industry
Industry-tested curriculum, taught by veterans with experience at top product companies!
