Data Scientist at Accenture

Skills and Experience🙋

Skills💡

Data scientists have been one of the world's most highly prized and in-demand careers for the past five years. Demand began to increase in every area as soon as firms began to see the value of data in their operations. Data science is the foundation for today's analytics, mining or extraction, NLP, ML, AI, and other enterprises.

Some key skills are mentioned below to become a data scientist at Accenture.

🏅Python

🏅Machine Learning

🏅Data Science

🏅Data Mining

🏅Statistics

🏅Cloud computing

🏅Operating system

🏅Computer science

🏅Strategy consulting

🏅Analytical

🏅Consulting

🏅Automation

🏅Business Modeling

🏅Data Analysis

🏅Management Consulting

🏅Monitoring

Now let's look at the experience required to become a data scientist at Accenture.

Experience🧑‍💻

🚨Possesses at least three years of relevant expertise in industry or consultancy, including developing and applying advanced analytics/machine learning algorithms.

🚨Know at least one programming language, such as R, Python, PySpark, or SQL.

🚨Become familiar with various Data Science and Machine Learning ideas and algorithms, including clustering, regression, classification, forecasting, neural networks, hyperparameters optimization, and NLP.

🚨Recognize that Data Engineering is one of the essential elements of successful analytics delivery and can contribute at any stage of the creation of an analytical model.

🚨Possess knowledge or interest in executing analytical projects on leading Cloud platforms like GCP, Azure, and AWS. 

🚨You are aware of the ML model lifecycle. A plus would be actual expertise using model lineage tools like MLflow and containerization tools like Docker and Kubernetes.

🚨A plus is having prior experience working in a client-facing or consulting environment.

Now you know all the critical information needed for the job. So what's left? The answer is preparation strategy and all the relevant resources. Don't worry; we have covered that part in the next section of this article.

Now, we will look into the preparation strategy of a data scientist at Accenture.

Preparation Strategy📚

Before becoming a data scientist, you should know why you want to become one. That mindset will provide you with a driving force in critical moments. You can refer to the blog here for a guide.

After you have decided, we will help you to kickstart your journey. 

Motivate yourself to learn data science and create some amazing ideas using it. Practice it consistently and begin to understand new concepts in data science one at a time. Before beginning your trip, it will be wise to participate in some workshops or conferences on data science. Set a clear goal for yourself and work toward it.

Now I will provide you with a roadmap, and please follow it to avoid confusion.

Mathematics🚀

Math aptitude is crucial since it aids in our knowledge of numerous machine learning and data science methods. Be equipped with fundamentals like linear algebra, vector calculus, matrix, geometry, etc. You can refer to the blog here for more information.

Probability🚀

It is considered one of the prerequisites for mastering machine learning. Refer to the blog here for more information.

Statistics🚀

Understanding the concepts of statistics is important for data analysis. Refer here for more knowledge.

Language🚀

Programming principles like data structures and algorithms must be well-understood. Python, R, Java, and Scala are the programming languages used. In some situations where efficiency is crucial, C++ can also be helpful. A good grasp of libraries like numpy, pandas, and tensorflow is very important to excel in data science.

Databases🚀

Having command over the databases is crucial too. You can learn about databases like SQL and MySQL.

Machine Learning🚀

Every year, new developments are made in machine learning (ML), one of the most important components of data science and the most popular research topic. At the very least, one must be familiar with the fundamental algorithms of supervised and unsupervised learning. Python and R have various libraries that can be used to implement these techniques. Refer to the blog here for a better understanding.

Deep Learning🚀

Well, you can't be confident in your skills if you aren't confident about deep learning. Having a thorough understanding of all the networks is important. Refer to the blog here for more understanding.

Feature Engineering🚀

It is the most important part of any data science project. You can't achieve anything without learning about feature engineering.

Others🚀

Having domain knowledge about the project, deployment skills, and skilled in data visualization tools are some of the factors that decide your expertise as a data scientist. So, learning about them is crucial too.

Lastly, keep practicing; you can't be an expert if you can't practice. Remember that these are the building blocks of your career as a data scientist. I would suggest you read as many articles' documentation as possible. Reading expands your horizon even far.

Career Map📈

The career of the data scientist starts with 🏄🏻‍♀️.

Junior Data Scientist🏄🏻

Junior data scientist is the starting position. It is regarded as the entry-level where a candidate joins after completing the selection process.

Data Scientist🏄🏻‍♂️

Data scientist level is achieved typically after getting 1 to 3 years of experience as a junior data scientist. The data scientist should be an expert in data analysis, analytical skills, research, python, R, SQL, machine learning, communication skills, and teamwork.

Senior Data Scientist🏂🏻

Senior data scientist level is usually achieved after 3 to 5 years as a data scientist. A senior data scientist understands the overall business picture. They have expertise in the best types of data analytics technologies, maintain budgets and prevent fraud.

Principal Data Scientist👨‍✈️

Experience of ten or more years is required to become a principal data scientist. This position is the most experienced among all data science team members.

Note⚠️ - The career path of a data scientist may vary a little from company to company.

So buckle up, fellow data scientist; you are the future.

Resources for Preparation🚨

Alright! Here are the resources to help you prepare for your recruitment exam and interview.

Interview Resources🛒

• Interview Puzzles.
• Interview Questions.
• Problems.
• Interview preparation.
• Data science/machine learning.
• Contests.
• Interview Sessions.

Technical Resources🛒

• Data Structures and Algorithms.
• Python.
• Data Warehousing and Data Mining.
• Data Science Course.
• CN Library.
• Software Testing
• Software Testing Methods

Aptitude Resources🛒

• Aptitude.
• Data Interpretation.
• Percentages.
• Time and Work.
• Permutations and Combinations.
• Probability.
• Ratio and Proportions.
• Order and Ranking.
• Cause and Effect.
• Error Detection and Correction.
• Aptitude | Learn & Practice.
• Aptitude Preparation Course.

Other Resources🛒

• Difference between Data Science And Machine Learning.
• Best Data Science Bootcamp Preparation Programs: A Guide.
• R Programming Language
• How Can Data Science with Python Kick-start Your Career?
• Big Data vs. Data Science
• It Certifications

Well, that's the end of the article. I have added some FAQs to clear up any doubts.

Frequently Asked Questions

What are the working hours at Accenture?

The work hours at Accenture were described as flexible by 67% of the workforce. Contrarily, 33% said it was strict, with the majority citing the hours of 9 am to 6 pm and 10 am to 7:30 pm.

What does Accenture do?

Business strategy, technology strategy, operations strategy, as well as technology, business, and management consulting services, are all offered by Accenture Strategy and Consulting. 

How much does a data scientist at Accenture make?

Accenture Data Scientist salary in India ranges between ₹5.4 Lakhs to ₹23.0 Lakhs with an average annual salary of ₹12.3 Lakhs.

Does Accenture use machine learning?

We harness the strength of fields like strategy, deeply functional and industry knowledge, and competence in deep machine learning, data science, and data architecture and engineering.

Who is a data scientist?

Data scientists are a new breed of analytical data experts with the technical know-how to address complicated issues and the curiosity to investigate what issues are at stake. They have elements of mathematicians, computer scientists, and trend-spotters.

Conclusion

This blog covered the Job role of a data scientist at Accenture. We went through responsibilities as Data Scientists with salary and perks, skills and experience required, a Career path as Data Scientists at Accenture, and a roadmap to becoming a Data Scientist at Amdocs. To learn more, check out our articles on

• Accenture Preparation Guide
• Accenture Innovation Challenge
• Ways to Join Accenture
• Job Opportunities at Accenture
• 30-day placement strategy for Accenture

That's all from the article. I hope you all like it.