Data analyst jobs can be found throughout a diverse mix of companies and industries. Any company that uses data needs data analysts to analyze it. Some of the top jobs in data analysis involve using data to make investment decisions, target customers, assess risks or decide on capital allocations.
Jobs in the data analytics sector are plentiful, salaries are high, and the career paths you can take are abundant. Data analytics offers a wide variety of opportunities across industries and corporate levels. As such it can be difficult to pinpoint salary and growth expectations. See the two of the most sought after job mentioned below:
Top 10 Data Analytics Courses:
1.Data Analytics for Lean Six Sigma
i) Data and DMAIC
ii) Descriptive statistics
iii) Hypothesis testing and Causality
iv) Introduction to ANOVA
v) Introduction to data analytics for lean six sigma
vi) Introduction to lean six sigma
vii) Introduction to Minitab: installing and loading data
viii) Kruskal-Wallis test
ix) Normal, lognormal, and Weibull distribution
x) Organizing data
xi) Pareto analysis
xii) Population vs. sampling
xiii) Probability plot and empirical CDF
xiv) Selecting CTQs
xv) Visualizing numerical and categorical data
2.Introduction to Data Analytics for Business:
i)Analytical organizations: roles and structures
ii) Aggregating and sorting data in SQL
iii) Big data & the cloud
iv) Conceptual business models
v) Data analytics tools
vi) Data captured by source systems
vii) Data extraction using SQL
viii) Data governance, privacy, and quality
ix) Data storage and databases
x) Extending SQL queries using operators
xi) Introduction to SQL
xii) The Information-Action Value Chain
xiii) The relational database
xiv) Virtualization, Federation, and In-Memory Computing
3.Beginner Statistics for Data Analytics –
i) Coefficient of variation
ii) Correlation and causation
iii) Creating and understanding a regression
iv) Fundamentals of statistics
v) Inferential statistics: probability distribution, normal distribution, Central Limit theorem, estimates, and confidence interval estimate
vi) Introduction to regression analysis
vii) Introduction to standard deviation and variance
viii) Mean, median, and mode
ix) Understanding and creating histograms
i) A crash course in Python
ii) Data cleaning
iii) Data grouping
iv) Data visualization with Pandas
v) Import and export data from Pandas
vi) Installing and setting up Python
vii) Introduction to data analysis
viii) Introduction to the data frame
ix) Introduction to Pandas and NumPy
x) Introduction to series
xi) Working with text data
5.Beginner’s Guide to Data & Data Analytics:
i) Classification of data analytics tools
ii) Data pipelines
iii) Data types, files, and formats
iv) Introduction to data
v) Key data analytics concepts and terminology
vi) Roles and skills of data professionals
vii) The data analytics “Tool Triangle.”
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6.Advanced Business Analytics Specialization:
i) Introduction to Data Analytics for Business (Course 1)
ii) Predictive Modeling and Analytics (Course 2)
iii) Business Analytics for Decision Making (Course 3)
iv) Communicating Business Analytics Results (Course 4)
v) Advanced Business Analytics Capstone (Course 5)
7.R Level 1 - Data Analytics with R:
i) Creating objects
ii) Data types
iii) Functions in R
iv) Graphs in R
v) Introduction to the R programming language
vii) Using the R Commander GUI
viii) Working with strings
8.Health Information Literacy for Data Analytics Specialization:
i) Healthcare Data Literacy (Course 1)
ii) Healthcare Data Models (Course 2)
iii) Healthcare Data Quality and Governance (Course 3)
iv) Analytical Solutions to Common Healthcare Problems (Course 4)
9.Data Analytics: SQL for newbs, beginners, and marketers:
i) Aggregating, grouping, and sorting
ii) Basic SQLcomands
iii) Basics of SQL
iv) Importing data on Windows
v) Increasing speed using indexes
vi) Installing SQLite on Linux, macOS, and Windows platforms
vii) Joining and merging tables
viii) Overview of SQL databases
ix) Spark SQL
10.Social Media Data Analytics:
i) Analyzing social media data using Python
ii) Analyzing social media data using R
iii) Analyzing structured data
iv) Data visualization
v) Introduction to Python programming
vi) Introduction to R
vii) Python for Data Analysis, Econometrics, and Statistics
viii) Structured vs. unstructured data
ix) Twitter libraries
x) Using Python for extracting data from Twitter and YouTube
What Do Data Analysts Do?
A data analyst collects, processes, and performs statistical analysis of data, i.e., makes the data useful in one way or another way. They help other people make the right decisions and prioritize the raw data that has been collected to make work easier using specific formulas and applying the right algorithms.
Data analysts take mountains of data and probe it to spot trends, make forecasts, and extract information to help their employers make better-informed business decisions. The career path you take as a data analyst depends in large part on your employer. Data analysts work on Wall Street at big investment banks, hedge funds, and private equity firms.
Many companies also label data analysts as information scientists. This classification typically involves working with a company’s proprietary database. Many information scientists work with core database infrastructures thus also gaining skills in other applicable technical areas such as data infrastructure building and development. Technology companies are unique because as technology changes rapidly, the dynamic of the company often changes too. Departments are constantly being created to tackle new challenges and pursue new market opportunities.
Career Paths Of Data Analyst:
Below is a list of some of the many different roles that you may encounter when searching for or considering data analysis.
Corporate strategy analyst: this type of role will focus on analyzing company wide data and advising management on strategy direction. This role may also be focused on mergers and acquisitions.
Business analyst: Analyzes business specific data.
Management reporting: Reports data analytics to management on business functions.
Budget analyst: Focuses on the analysis and reporting of a specified budget.
Compensation and benefits analyst: Usually part of a human resources department that analyzes employee compensation and benefits data.
Insurance underwriting analyst: Analyzes individual, company, and industry data for decisions on insurance plans.
Web analytics: Analyzes a dashboard of analytics around a specific page, topic focus, or website comprehensively.
Sales analytics: Focuses on sales data that helps to support, improve, or optimize the sales process.
Actuary: Analyzes mortality, accident, sickness, disability, and retirement rates to create probability tables, risk forecasting, and liability planning for insurance companies.
Fraud analytics: Monitors and analyzes fraud data.
Business product analyst: Focuses on analyzing the attributes and characteristics of a product as well as responsibility for advising management on the optimal pricing of a product based on market factors.
Credit analytics: The credit market offers a wide need for analytics and information science in the areas of credit reporting, credit monitoring, lending risk, lending approvals, and lending analysis.
Social media data analyst: Social media and growing tech companies rely on data to build, monitor, and advance the technology and offerings that social media platforms rely on.
Machine learning analyst: Machine learning is a developing technology that involves programming and feeding machines to make cognitive decisions. Machine learning analysts may work on a variety of aspects including data preparation, data feeds, analysis of results, and more.
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Data Analyst Qualifications:
A bachelor's degree is needed for most entry-level jobs, and a master's degree will be needed for many upper-level jobs. Most analysts will have degrees in fields like math, statistics, computer science, or something closely related to their field. Strong math and analysis skills are needed.
Skills for Data Analysts:
Effective data analysts possess a combination of technical skills and leadership skills.
1- Technical skills include knowledge of database languages such as SQL, R, or Python; spreadsheet tools such as Microsoft Excel or Google Sheets; and data visualization software such as Tableau or Qlik. Mathematical and statistical skills are also valuable to help gather, measure, organize, and analyze data.
2- Leadership skills prepare a data analyst to complete decision-making and problem-solving tasks. These abilities allow analysts to think strategically about the information that will help stakeholders make data-driven business decisions and to communicate the value of this information effectively.
Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems.
Demonstrating the significance of their work in the context of local, national, and global trends that impact both their organization and industry.
Using statistical tools to interpret data sets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive
Mining data from primary and secondary sources, then reorganizing said data in a format that can be easily read by either human or machine.
Preparing reports for executive leadership that effectively communicate trends, patterns, and predictions using relevant data.
Collaborating with programmers, engineers, and organizational leaders to identify opportunities for process improvements, recommend system modifications, and develop policies for data governance.
Creating appropriate documentation that allows stakeholders to understand the steps of the data analysis process and duplicate or replicate the analysis if necessary.
Being a Data analyst involves understanding various aspects such as finance, administration, and management. It is one of the most lucrative professions change as the organizational decisions are completely based on the data acquired. Data Analyst certifications are the most sought after certifications for entry-level as well as experienced professionals. There are many renowned institutes like Yoda learning offering certifications for various tools that are used for business analytics. Be a Data Analyst and take your career to the next level.
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