Breaking into Data Analytics: Tips and Strategies for Aspiring Data Analysts

Breaking into Data Analytics: Tips and Strategies for Aspiring Data Analysts

Data analytics is analyzing and interpreting data to draw meaningful insights and conclusions. In today’s data-driven world, data analytics has become crucial for businesses to make informed decisions and gain a competitive edge. It uses statistical and computational techniques to analyze large datasets, identify patterns, and make predictions.

Data analytics is essential because it enables organizations to identify trends, make accurate forecasts, and gain insights into customer behavior. Businesses can make data-driven decisions, improve efficiency, and increase profitability by leveraging data analytics.

Anyone interested in working with data can benefit from data analytics. Whether you’re a recent graduate, a mid-career professional, or an executive, data analytics skills can help you progress your career and achieve your goals.

What is Data Analytics and Why is it Important?

Data analytics is the approach of analyzing and interpreting data to extract meaningful insights and information. It involves using various techniques and tools to examine large datasets, identify patterns, and draw conclusions. It has become increasingly important in today’s business landscape, enabling organizations to make informed decisions based on data-driven insights.

Data analytics is crucial for businesses because it helps them to identify trends, make accurate forecasts, and gain insights into customer behavior. With the help of data analytics, organizations can improve their operations, optimize their resources, and increase profitability. It can also help businesses identify improvement areas, streamline their processes, and stay ahead of the competition.

Data analytics is a growing field with a high demand for skilled professionals. There are various career opportunities in data analytics, including data analyst, business analyst, data scientist, data engineer, and more. These roles require a mix of technical and soft skills, such as data analysis, programming, communication, problem-solving, and critical thinking.

Essential Skills and Knowledge for Aspiring Data Analysts

To become a successful data analyst, there are a variety of technical and non-technical skills that you need to possess. Technical skills include knowledge of programming languages, databases, data visualization tools, and statistical analysis. Non-technical skills include communication, problem-solving, and critical thinking.

It’s also important to have domain knowledge in the industry you’re working in. For example, it’s important to understand healthcare terminology and regulations if you’re analyzing data for a healthcare organization. This will enable you to ask the right questions and draw meaningful insights from the data.

Many resources are available for acquiring the necessary skills and knowledge for data analytics. Online courses, boot camps, and degree programs are all viable options. Additionally, many free resources are available, such as YouTube tutorials and open-source software.

Tips and Strategies for Breaking into Data Analytics

Breaking into the field of data analytics can be challenging, but with the proper strategies and mindset, you can achieve your goals. Here are some tips and techniques to help you break into data analytics:

1. Identify your career goals and paths

Before starting your journey in data analytics, you must identify your career goals and the path you want to take. Do you want to become a data analyst, data scientist, or data engineer? Understanding your goals will help you focus your efforts and choose the right resources and tools.

2. Build a strong foundation in statistics and programming

You must have a reliable statistics and programming foundation to succeed in data analytics. Familiarize yourself with programming languages like Python and R, and learn statistical analysis techniques like regression analysis and hypothesis testing.

3. Gain experience through internships and projects

Internships and projects are excellent ways to gain practical experience in data analytics. Seek internships in data-driven organizations and participate in data analytics projects on platforms like Kaggle.

4. Network and build professional relationships

Networking is essential in any field, and data analytics is no exception. Attend industry events, join online communities, and connect with other professionals in the field. Building relationships with others can lead to job opportunities and valuable insights.

5. Create a strong portfolio and resume

Your portfolio and resume should showcase your skills, knowledge, and experience in data analytics. Include projects you’ve worked on, data visualizations you’ve created, and any relevant coursework or certifications.

By following these tips and strategies, you can position yourself for success in the field of data analytics. You can break into this exciting and growing field with determination, hard work, and a willingness to learn.

Data Science and Data Analytics Courses for Aspiring Data Analysts

Taking data science and data analytics courses can be an excellent way to gain the necessary skills and knowledge to break into the field of data analytics. Here are some pivotal points to consider when exploring data science and data analytics courses:

Overview of data science and data analytics courses

Data science and data analytics courses provide training in statistical analysis, data visualization, programming, and other relevant topics. They can be taken online or in person and vary in length and depth.

Benefits of taking data science and data analytics courses

Data science and data analytics courses can provide a comprehensive education in the field, help you gain practical skills, and provide networking opportunities. They can also help demonstrate your dedication and expertise to potential employers.

Types of courses available for aspiring data analysts

Various types of data science and data analytics courses are available, including certificate programs, boot camps, online courses, and degree programs. Each has its own strengths and weaknesses and can be tailored to fit different skill levels. Two highly recommended programs are Great Learning’s Data Science Courses and Data Analytics Courses, which provide in-depth knowledge of concepts and hands-on experience in solving real-world problems.

Comparison of different courses available

Consider factors like cost, length, content, and instructor experience when choosing a course. Research reviews and ratings from previous students to get an idea of the quality of the course.

Recommended courses for different skill levels

For beginners, introductory courses in Python and statistics can be helpful. For intermediate learners, courses on machine learning, data visualization, and databases can be useful. Advanced learners may benefit from big data, data engineering, and data science research courses.

Wrapping Up

Data analytics is a rapidly evolving field and an incredibly rewarding career choice for those with the right skills and experience. With the right tips and strategies, aspiring data analysts can break into the field and position themselves for tremendous success. By understanding the essential skills and industry language, carefully planning their entry into the field, and leveraging contacts in the field, ambitious analysts can take the first steps in achieving their career goals and begin to make an impact within the data analytics industry.

AWS Certified Data Analytics – Specialty (DAS-C01) Exam Learning Path

AWS Certified Data Analytics – Specialty (DAS-C01) Exam Learning Path

  • Recently validated myself with the AWS Certified Data Analytics – Specialty (DAS-C01).
  • Data Analytics – Specialty (DAS-C01) has replaced the previous Big Data – Specialty (DAS-C01).
  • Big Data in itself is a very vast topic and with AWS services, there is lots to cover and know for the exam.
  • If you have worked on Big Data technologies including a bit of Visualization, it would be a great asset to pass this exam.

AWS Certified Data Analytics – Specialty (DAS-C01) exam basically validates

  • Define AWS data analytics services and understand how they integrate with each other.
  • Explain how AWS data analytics services fit in the data lifecycle of collection, storage, processing, and visualization.

Refer AWS Certified Data Analytics – Specialty Exam Guide for details

AWS Certified Data Analytics - Specialty DAS-C01 Domains

AWS Certified Data Analytics – Specialty (DAS-C01) Exam Resources

AWS Certified Data Analytics – Specialty (DAS-C01) Exam Summary

  • AWS Certified Data Analytics – Specialty exam, as its name suggests, covers a lot of Big Data concepts right from data transfer and collection techniques, storage, pre and post processing, analytics, visualization with the added concepts for data security at each layer.
  • AWS Certified Data Analytics – Specialty exam has 65 questions to be solved within a time limit of 170 minutes
  • Questions and answer options are pretty long, so need time to read through them to make sense of the requirements and filter out the answers
  • As the exam was online from home, there was no access to paper and pen but the trick remains the same, read the question and draw a rough architecture and focus on the areas that you need to improve. Trust me, you will be able to eliminate 2 answers for sure and then need to focus on only the other two. Read the other 2 answers to check the difference area and that would help you reach to the right answer or atleast have a 50% chance of getting it right.
  • Be sure to cover the following topics
    • Whitepapers and articles
    • Analytics
      • Make sure you know and cover all the services in depth, as 80% of the exam is focused on topics like Glue, Kinesis and Redshift.
      • Glue
        • DAS-C01 covers Glue in detail. This is one of the newly added service as compared to Big Data -Specialty exam
        • Understand Glue as a fully-managed, extract, transform, and load (ETL) service
        • Glue natively supports RDS, Redshift, S3 and databases on EC2 instances.
        • Glue provides Glue crawlers to crawl data and helps discover and create schema in Glue Data Catalog
        • Glue supports Job Bookmark that helps track data that has already been processed during a previous run of an ETL job by persisting state information from the job run. Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data or duplicate records.
      • Elastic Map Reduce
        • Understand EMR in depth
        • Understand EMRFS (hint: Use Consistent view to make sure S3 objects referred by different applications are in sync)
        • Know EMR Best Practices (hint: start with many small nodes instead on few large nodes)
        • Know EMR Encryption options
          • supports SSE-S3, SS3-KMS, CSE-KMS and CSE-Custom encryption for EMRFS
          • doesn’t support SSE-C  encryption
          • supports LUKS encryption for local disks
          • supports TLS for data in transit encryption
        • Know Hive can be externally hosted using RDS, Aurora and AWS Glue Data Catalog
        • Know also different technologies
          • Presto is a fast SQL query engine designed for interactive analytic queries over large datasets from multiple sources
          • Spark is a distributed processing framework and programming model that helps do machine learning, stream processing, or graph analytics using Amazon EMR clusters
          • Zeppelin/Jupyter as a notebook for interactive data exploration and provides open-source web application that can be used to create and share documents that contain live code, equations, visualizations, and narrative text
          • Phoenix is used for OLTP and operational analytics, allowing you to use standard SQL queries and JDBC APIs to work with an Apache HBase backing store
      • Kinesis
        • Understand Kinesis Data Streams and Kinesis Data Firehose in depth
        • Know Kinesis Data Streams vs Kinesis Firehose
          • Know Kinesis Data Streams is open ended on both producer and consumer. It supports KCL and works with Spark.
          • Know Kinesis Firehose is open ended for producer only. Data is stored in S3, Redshift and ElasticSearch.
          • Kinesis Firehose works in batches with minimum 60secs interval and is near-real time.
          • Kinesis Firehose supports transformation and  custom transformation using Lambda
        • Understand Kinesis Encryption (hint: use server side encryption or encrypt in producer for data streams)
        • Know difference between KPL vs SDK (hint: PutRecords are synchronously, while KPL supports batching)
        • Kinesis Best Practices (hint: increase performance increasing the shards)
      • Elasticsearch
        • Know ElasticSearch is a search service which supports indexing, full text search, faceting etc.
        • Elasticsearch can be used to analysis and supports visualization using Kibana which can be real time.
      • Redshift
        • Understand Redshift in depth
        • Understand Redshift Advanced topics like Workload Management, Distribution Style, Sort key
        • Understand Redshift Spectrum which allows querying data in S3 without loading existing Redshift cluster. It also helps querying S3 data with Redshift data.
        • Know Redshift Best Practices w.r.t selection of Distribution style, Sort key, importing/exporting data
          • COPY command which allows parallelism, and performs better than multiple COPY commands
          • COPY command can use manifest files to load data
          • COPY command handles encrypted data
        • Understand Redshift Resizing cluster options (elastic resize did not support node type changes before, but does now)
        • Know Redshift views to control access to data.
      • Athena
        • serverless, interactive query service to analyze data in S3 using standard SQL
      • QuickSight
        • Understand QuickSight
        • Know Visual Types (hint: esp. plotting line, bar and story based visualizations)
        • Know Supported Data Sources (hint: supports files)
        • QuickSight provides direct integration with Microsoft AD
        • QuickSight supports Row level security using dataset rules
        • QuickSight supports ML insights as well
      • Know Data Pipeline for data transfer
    • Security, Identity & Compliance
    • Management & Governance Tools
      • Understand AWS CloudWatch for Logs and Metrics. Also, CloudWatch Events more real-time alerts as compared to CloudTrail