Remember: These skills are a good starting point but employers may prioritize more skills depending on their specific needs.

SQL:

SQL is a programming language used to manage and manipulate relational databases, including inserting, updating, deleting, and retrieving data. It is used in various applications, such as business intelligence, data analysis, and web development.

As an entry-level data analyst, you should have following skills:

• Basic SQL syntax and querying: Entry-level data analysts should have a strong understanding of basic SQL syntax and be able to write simple queries to extract data from databases.
• Filtering and sorting data: Entry-level data analysts should be able to use the WHERE and ORDER BY clauses to filter and sort data based on specific criteria.
• Joins: Entry-level data analysts should be able to use JOIN clauses to combine data from multiple tables.

SQL Courses for Entry-Level Data Analyst:

As a mid-career data analyst, you should have following skills:

• Advanced querying: Mid-career data analysts should have a strong understanding of advanced SQL syntax and be able to write complex queries that involve subqueries, aggregate functions, and multiple joins.
• Data modeling and database design: Mid-career data analysts should be able to design and implement data models that are optimized for performance and scalability. Learn valuable Data Modeling techniques that can streamline your data management processes.
• Stored procedures and functions: Mid-career data analysts should be able to create stored procedures and functions that encapsulate complex business logic and automate repetitive tasks.

As an senior-level data analyst, you should have following skills:

• Performance optimization: Senior data analysts should have a deep understanding of database performance tuning and be able to optimize complex queries and data models for performance and scalability.
• Data architecture and governance: Senior data analysts should be able to design and implement data architectures that ensure data quality, consistency, and security across the organization.
• Data warehousing and business intelligence: Senior data analysts should be able to design and implement data warehousing and business intelligence solutions that enable data-driven decision-making across the organization.
• They should have experience with ETL (extract, transform, load) processes, data integration, and data visualization tools.

SQL Courses for Mid-Career & Senior-Level Data Analyst:

Excel:

Microsoft Excel is a spreadsheet software program used for organizing, analyzing, and manipulating numerical data.

It’s a great tool for data analysts who want to organize, analyze, and manipulate numerical data. It has lots of features and functions that make it easy to manage large amounts of data and draw insights from it. If you want to improve your Excel skills as a data analyst, you can choose among the following listed online courses.

Excel Courses for Data Analyst:

Moreover you can follow here for a complete list of best Excel certification courses and best online Excel courses. Excel can also be used with other tools like Tableau, which we compared in our Tableau vs Excel article.

Statistics:

Statistics is the discipline that deals with the collection, analysis, interpretation, presentation, and organization of numerical data.

As an entry-level data analyst, you should have a good foundation in basic statistics concepts and be able to apply them to analyze and interpret data. Some of the key statistical skills you should have include descriptive and Inferential statistics, regression analysis, probability theory, data visualization and manipulation etc.

Entry-Level Data Analyst Statistics Courses:

As a mid-career data analyst, you should have a strong understanding of the basic statistics concepts and skills required for entry-level roles, as well as more advanced statistical techniques. Some of the key statistical skills you should have at this stage in your career include multivariate statistics, Time series analysis, Experimental design, Bayesian statistics and more.

As a senior data analyst, a deep understanding of statistical models is crucial for designing and implementing effective data analysis strategies, and for communicating statistical findings to stakeholders. It involves understanding the assumptions and limitations of different models, selecting appropriate models for different types of data and research questions, and interpreting and communicating the results of statistical models to non-technical audiences.

Statistical Models:

Statistical models are mathematical models that are used to describe the relationships between variables in a dataset. They are used in statistical analysis to help researchers understand the patterns and relationships in their data, and to make predictions about future observations.

A statistical model typically involves a set of assumptions about the data, and a set of parameters that describe the relationships between variables. The parameters are estimated from the data using statistical techniques such as maximum likelihood estimation or Bayesian inference.

There are many different types of statistical models, depending on the nature of the data and the research question being investigated. Some examples include:

• Linear regression models: Used to model the relationship between a dependent variable and one or more independent variables.
• Generalized linear models: Used to model relationships between variables when the dependent variable is not normally distributed, such as binary or count data.
• Time series models: Used to model patterns and trends in time-dependent data.
• Clustering models: Used to group similar observations together based on patterns in the data.
• Decision tree models: Used to model decision-making processes, such as classification or prediction tasks.
• Neural network models: Used to model complex, non-linear relationships between variables.

Statistics Courses for Mid & Expert Level Data Analyst:

If you are looking for a complete list of statistics courses that come with certificates, including courses on business statistics and statistics for data science, you can check out our website by following the link provided.

Data Cleaning & Data Wrangling:

Data cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. This involves detecting and removing irrelevant, duplicate, or incorrect data, and transforming the data into a consistent and usable format for analysis.

Data cleaning is a critical step in the data analysis process as it ensures that the data used for analysis is accurate, complete, and reliable. Without data cleaning, the analysis results may be skewed or incorrect, leading to flawed conclusions and ineffective decision-making.

Data wrangling, also known as data munging or data preparation, is the process of transforming and preparing raw data into a format that is suitable for analysis. This involves a variety of tasks, including cleaning, transforming, merging, and reshaping data.

Data wrangling is an important step in the data analysis process because it allows analysts to work with data that is in a format that can be easily analyzed. It can also help to ensure that the data is consistent, complete, and accurate.

Data wrangling often involves using programming languages such as Python or R to automate the process of cleaning and transforming the data. This can be a time-consuming process, but it is essential for ensuring that the data is ready for analysis.

How Data Wrangling is different from Data Cleaning?

Data cleaning and data wrangling are both important steps in the data analysis process, but they refer to different aspects of preparing data for analysis.

Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. This includes removing or correcting duplicate, irrelevant, or incorrect data, and ensuring that the data is complete, accurate, and consistent. The goal of data cleaning is to ensure that the data is usable for analysis.

Data wrangling, on the other hand, involves transforming and preparing raw data into a format that is suitable for analysis. This includes tasks such as merging, reshaping, and transforming data, as well as addressing missing data and dealing with outliers. The goal of data wrangling is to create a dataset that is optimized for analysis.

In other words, data cleaning is a subset of data wrangling. Data cleaning focuses on removing errors and inconsistencies in the data, while data wrangling involves preparing the data for analysis by transforming it into a more usable format.

Data Visualization:

Data visualization is the graphical representation of data and information using visual elements such as charts, graphs, and maps to communicate complex information clearly and effectively.

At entry-level, a data analyst should have a basic understanding of data visualization principles and tools. Some of the key skills include:

• Understanding data types: Knowing how to effectively represent different types of data, such as categorical, numerical, and ordinal data.
• Choosing the right chart type: Understanding when to use different chart types, such as bar charts, line charts, scatterplots, and histograms.
• Design principles: Knowing how to apply basic design principles, such as color theory, typography, and layout, to create effective visualizations.
• Familiarity with data visualization tools: Knowing how to use basic data visualization tools such as Excel, Tableau, or Power BI.

At mid-career level, a data analyst should have a strong understanding of data visualization principles and tools, and should be able to create more complex and sophisticated visualizations. Some of the key skills include:

• Advanced chart types: Knowing how to create more complex chart types such as heatmaps, treemaps, and network diagrams.
• Interactive visualizations: Knowing how to create interactive visualizations using tools such as Tableau or D3.js.
• Data storytelling: Knowing how to effectively communicate insights through visual storytelling and creating dashboards.
• Programming skills: Knowing how to use programming languages such as Python, R or JavaScript to create custom visualizations or automate the visualization process.

At senior level, a data analyst should have a deep understanding of data visualization principles and tools, and should be able to create complex and sophisticated visualizations that can communicate insights effectively to a variety of stakeholders. Some of the key skills include:

• Advanced programming skills: Knowing how to use programming languages to create custom visualizations or automate the visualization process.
• Data storytelling and data journalism: Being able to tell compelling data-driven stories using visualizations and creating data-driven articles for stakeholders.
• Visual analytics: Being able to use advanced visual analytics techniques, such as visual clustering or dynamic visualization techniques, to explore complex and large datasets.
• Data visualization leadership: Being able to lead and mentor other data analysts to create effective and compelling visualizations for a variety of stakeholders.

R Language:

R is a programming language and software environment for statistical computing and graphics, widely used for data analysis, data visualization, and statistical modeling.

Python:

Python is a high-level, interpreted programming language that is widely used in data analysis, machine learning, and scientific computing due to its powerful libraries, simplicity, and ease of use.

Machine Learning Algorithms & Techniques:

At mid-career level, a data analyst should have a good understanding of the fundamentals of machine learning and should be able to apply basic machine learning algorithms to solve business problems. Some of the key algorithms they should be familiar with include:

• Regression
• Decision trees
• Random Forest
• Naive Bayes
• Support Vector Machines (SVM)
• Clustering

Mid-career data analysts should also have experience in selecting and applying appropriate algorithms to solve specific business problems, optimizing hyperparameters, and interpreting the results.

At senior level, a data analyst should have a deep understanding of machine learning algorithms and should be able to apply advanced techniques to solve complex business problems. Some of the key algorithms they should be familiar with include:

• Gradient Boosting
• Neural Networks
• Deep Learning
• Principal Component Analysis (PCA)

In addition to these algorithms, senior data analysts should also have experience in data preparation, feature engineering, and model selection, as well as experience in leading data science projects and mentoring junior team members.

Curious to discover Advanced Data Engineering Training? Explore our comprehensive guide for a diverse range of courses and classes tailored to your expertise level.

It’s important to note that these skills lists are not exhaustive, and the specific algorithms that a data analyst should be familiar with will vary depending on the industry, organization, and job role.

Big Data Technologies:

Here are some skills that a senior data analyst should have in big data technologies:

• Hadoop: A senior data analyst should be familiar with Hadoop’s ecosystem, including HDFS, MapReduce, and YARN. They should be able to write MapReduce jobs in Java or Python, and understand how to optimize them for performance. Additionally, they should have experience working with Hadoop distributions such as Cloudera, Hortonworks, or MapR.
• Spark: A senior data analyst should be proficient in Apache Spark and its programming language, Scala or Python. They should be able to work with Spark’s RDDs, DataFrames, and Datasets, and understand how to optimize Spark jobs for performance. They should also have experience working with Spark’s machine learning and graph processing libraries.
• Kafka: A senior data analyst should be familiar with Apache Kafka and understand how it is used for streaming data processing. They should have experience working with Kafka topics, partitions, and consumer groups, and be able to write Kafka producers and consumers in Java or Python. They should also understand how to configure and tune Kafka for performance and reliability.
• SQL on Big Data: A senior data analyst should have experience working with SQL on big data platforms such as Hadoop and Spark. They should be familiar with SQL engines such as Hive and Impala, and be able to write complex SQL queries that join data from multiple sources.
• Data Ingestion and ETL: A senior data analyst should be familiar with data ingestion techniques for big data platforms, such as Flume, Sqoop, and NiFi. They should also understand how to design and implement ETL pipelines that extract, transform, and load data into big data platforms.
• Data Security and Governance: A senior data analyst should be familiar with big data security and governance best practices. They should understand how to configure and manage Kerberos authentication, as well as authorization and auditing policies. They should also be familiar with data governance frameworks such as Apache Atlas.

It’s important to note that different industry or organizations may require a bit different big data skills for senior data analysts. 

Data Storytelling:

Data storytelling is the process of using data to create a narrative or tell a story. It involves using data visualization techniques and data analysis to communicate insights and key messages to an audience.

The goal of data storytelling is to make data more accessible and understandable to a wider audience. By presenting data in a way that is easy to digest, data storytelling can help to communicate complex information and insights in a more compelling way.

Data storytelling often involves using tools such as charts, graphs, and infographics to present data visually. It also involves selecting the right data points and presenting them in a way that supports the narrative or story being told.

Data storytelling is used in a variety of contexts, including business, journalism, and public policy. It is an effective way to communicate insights and recommendations to stakeholders, and can help to drive decision-making and action based on data.

Ready to enhance your data narrative skills? Check out our recommended courses with certification to level up your storytelling game.

At entry level, a data analyst should be able to:

• Identify the key insights from data analysis and present them in a clear and concise manner.
• Create basic visualizations to communicate data insights.
• Explain data analysis findings to non-technical stakeholders in a simple and understandable way.

At mid-career level, a data analyst should be able to:

• Develop a narrative that ties together data insights to tell a story.
• Use data visualization techniques to highlight key insights and support the narrative.
• Identify and address potential data biases or limitations in the analysis.
• Tailor the presentation of data insights to different stakeholders and their needs.

At senior level, a data analyst should be able to:

• Develop and communicate data-driven insights that influence strategic decision-making.
• Create compelling data visualizations that effectively communicate insights and support the narrative.
• Tell stories with data that connect with audiences on an emotional level and drive action.
• Identify and address potential data biases or limitations in the analysis and communicate them clearly to stakeholders.

Leadership & Management:

As a mid-career data analyst with experience in project management and leading data-driven projects, the following skills are important to have:

• Planning and Organizing: The ability to plan and organize projects, set timelines, and manage resources effectively is essential. This involves defining project objectives, creating project plans, identifying deliverables, and estimating project costs and resources.
• Communication and Stakeholder Management: Effective communication is essential for managing stakeholders, building consensus, and ensuring project success. A mid-career data analyst should be able to communicate complex data insights in simple terms to both technical and non-technical stakeholders, and manage their expectations throughout the project lifecycle.
• Risk Management: A mid-career data analyst should have experience identifying and managing risks associated with data-driven projects. This involves developing a risk management plan, assessing risks, and developing contingency plans to mitigate them.
• Project Monitoring and Control: A mid-career data analyst should be able to monitor project progress against plans, identify variances, and take corrective action as needed. This involves tracking project milestones, managing project changes, and ensuring project quality.
• Team Management: A mid-career data analyst should have experience managing project teams, including both technical and non-technical staff. This involves assigning roles and responsibilities, providing direction and guidance, and managing team performance.
• Budget Management: A mid-career data analyst should have experience managing project budgets, including tracking project expenses, forecasting costs, and ensuring that the project stays within budget.
• Problem-Solving and Decision-Making: A mid-career data analyst should be able to identify problems and make data-driven decisions. This involves analyzing data, identifying trends, and developing solutions that address project challenges.

Leadership and communication skills required for a senior data analyst:

Leadership & Communication Skills:

• Mentorship and Training: A senior data analyst should have strong leadership and management skills, including the ability to mentor and train junior analysts. This involves providing guidance and feedback, setting expectations, and creating opportunities for professional development. A senior data analyst should be able to identify the strengths and weaknesses of their team members and provide the necessary support to help them grow.
• Team Building and Collaboration: A senior data analyst should be able to build effective teams and foster collaboration. This involves understanding the strengths and weaknesses of team members, defining roles and responsibilities, and creating a positive work environment where team members can collaborate effectively.
• Performance Management: A senior data analyst should be able to manage team performance effectively. This involves setting goals and objectives, monitoring progress, providing feedback, and taking corrective action when necessary.
• Strategy and Planning: A senior data analyst should be able to develop and implement data-driven strategies and plans that support organizational goals. This involves understanding business needs, identifying opportunities for data-driven insights, and developing plans to leverage data to achieve business objectives.

Communication Skills and Executive Presentation:

• Effective Communication: A senior data analyst should have strong communication skills and be able to effectively communicate data insights to both technical and non-technical stakeholders. This involves being able to distill complex data into simple and actionable insights that can be understood by a range of audiences.
• Storytelling: A senior data analyst should be able to tell compelling stories with data that engage and persuade stakeholders. This involves understanding the context and audience of the data, identifying key insights, and crafting a narrative that highlights the importance and relevance of the data.
• Executive Presentation: A senior data analyst should be able to present data insights to senior executives and stakeholders. This involves understanding the business context, presenting data insights in a clear and concise manner, and being able to answer questions and provide context as needed. A senior data analyst should be able to translate technical data insights into business outcomes and recommendations that can inform strategic decision-making.

In conclusion, becoming a data analyst requires a specific set of skills that can be developed over time with the right training and education. By following our data analyst roadmap, you can learn the skills necessary to succeed in this field at any level, whether you are just starting out or are a seasoned professional.

By mastering skills like SQL, data visualization, and data analysis tools like R or Python, statistical models, machine learning algorithms, big data technologies you can gain a competitive edge in the job market and advance your career. We have compiled a comprehensive list of online courses and resources to help you acquire these skills and excel as a data analyst. With the right mindset and dedication, you can become a successful data analyst and make meaningful contributions to your organization.