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Data Analytics Certification Program

Become a Skilled and Job-Ready Data Scientist!

Unlock the power of data with our Data Analytics course, where you’ll learn to turn raw information into actionable insights. Gain skills in trend analysis, predictive modeling, and data visualization, preparing you for lucrative career opportunities in healthcare, marketing, retail, insurance, and technology. Join us and begin your journey toward a fulfilling career in the thriving field of data analytics.

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Duration : 30+

Mastering Data Analytics Course Highlights

Mastering Data Analytics typically covers a broad range of topics essential for understanding and working effectively with data, including data manipulation, feature engineering, and model evaluation techniques.

Data Analytics Certification Course Learnings

Efficient data querying and manipulation techniques using SQL.

Master the core concepts and methodologies for effective data management.

Uncovering patterns and insights within datasets through EDA methodologies.

Introduction to BI tools like Tableau and Power BI for creating interactive reports.

Real-world case studies and projects for practical application of learned concepts.

Mastering Data Analytics Course Curriculam

1.1 Define the concept of data

1.2 Describe basic data variable types

  • Boolean, numeric, string

1.3 Describe basic structures used in data analytics

  • Tables, rows, columns, lists

1.4 Describe data categories

  • Qualitative, quantitative, structured, unstructured, metadata, big data

2.1 Import, store, and export data

  • Fundamental understanding of ETL (extract, transform and load) processes, data manipulation tools (SQL, R, Python, Microsoft Excel including aspects of Power Query), and common data storage file formats (delimited data files, XML, JSON).

2.2 Clean data

  • Purpose and common practices (handling NULL, special characters, trimming spaces, inconsistent formatting, removing duplicates, imputing data, etc.); validating data

2.3 Organize data

  • Purpose and common practices (sorting, filtering, slicing, transposing, appending, truncating, etc.)

2.4 Aggregate data

  • Purpose and common practices (grouping, joining/merging, summarizing, pivoting, etc.)

3.1 Describe and differentiate between types of data analysis

  •  Descriptive analysis, diagnostic analysis, hypothesis testing, predictive analysis, prescriptive analysis

3.2 Describe and differentiate between data aggregation and interpretation metrics

  •  Searching, filtering, unique values, aggregate functions such as Sum, Max, Min, Count, Avg/Mean, Mode, Median, Std Dev

3.3 Describe and differentiate between exploratory data analysis methods

  • Identify data relationships, describe data drilling concepts (granularity, etc.), describe data mining concepts (anomalies, correlation analysis, patterns, outliers, etc.)

3.4 Evaluate and explain the results of data analyses

  • Calculate trends, determine expected values, interpret results of predictive models, p-values, tests, and regression analyses

3.5 Define and describe the role of artificial intelligence in data analysis

  • Define artificial intelligence, machine learning, and algorithm; describe how AI is used in data analysis; describe how machine learning algorithms are used in data analysis (Note: Specific algorithms are out of scope).

4.1 Report data

  •  Effectively display information in tables and charts; explain when and why to disaggregate data

4.2 Create visualizations from data

  •  Identify data visualization practices that minimize the potential for misinterpretation; identify visualization types that represent the underlying data structure and analysis questions (including comparison, time/trend, part-to-whole, relationship, distribution, correlation graphs, box and whisker diagram, scatter chart, scatter plot, bar chart, Sankey diagram, histogram, pie chart, column chart, etc.)

4.3 Derive conclusions from a data visualization

  • Translate a visual representation of data into words; identify differences between claims based on an analysis and its graphical representation

5.1 Describe data privacy laws and best practices

  •  GDPR, FERPA, HIPAA, IRB, PCI, etc.

5.2 Describe best practices for responsible data handling

  • Methods of handling PII, securing data, and protecting anonymity within small data sets; importance of anonymizing data; trade-offs when balancing interpretability and accuracy; shortcomings of making population-level generalizations with limited sample data

5.3 Given a scenario, describe types of bias that affect collection and interpretation of data

  •  Confirmation bias, human cognitive bias, motivational bias, sampling bias; selecting visualizations/data representations to avoid bias

Training And Package Fee

Training Fee

Designed to get you trained with the core knowledge.

Original price was: $999.00.Current price is: $899.00.

Job Seeker's Program Fee

Designed to make you job ready with knowledge, experience, and grooming.

$1400.00 $1150.00

One-On-One Training Course @ $1499

Data Analytics Course Outcomes

Proficiency in Data Handling Techniques.

Proficiency with Business Intelligence Tools.

Application of Machine Learning Algorithms.

Utilization of SQL and Database Management.

Career advancement in data-related roles.

What roles you can play?

Data Analyst

A Data Analyst is the one who Analyzes data to extract valuable insights and inform business decisions.

Data Engineer

A Data Enginner Designs, constructs, and maintains scalable data pipelines for efficient data processing and analysis.

Data Scientist

Utilizes advanced statistical analysis and machine learning techniques to extract insights and build predictive models.

Business Analyst

Evaluates business processes and performance using data-driven insights to improve efficiency and profitability.

Operations Analyst

An Operations Analyst Optimizes operational processes by analyzing data to improve efficiency and reduce costs.

Research Analyst

A Research Analyst Conducts in-depth research and analysis to support business strategy development and decision-making.

Know before you Start

The focus of this Data Analytics course is to equip students with the skills and knowledge necessary to effectively collect, analyze, and interpret data to drive informed decision-making and solve real-world problems. Through a combination of theoretical concepts, hands-on exercises, and practical applications, students will learn key techniques in data handling, statistical analysis, machine learning, and data visualization. The course emphasizes practical skills applicable across various industries and sectors, preparing students for roles such as data analyst, business analyst, or data scientist. Additionally, ethical considerations and privacy concerns in data analytics are addressed to ensure responsible data handling practices.

Basic math/statistics knowledge is required. Familiarity with Python or Excel is helpful but not mandatory.
  • Programming Languages: Python and R
  • Data Analysis Libraries: Pandas, NumPy, Scikit-learn (Python); tidyverse packages (R)
  • Data Visualization Tools: Matplotlib, Seaborn, Plotly
  • Database Management Systems: SQL
  • Business Intelligence Tools: Tableau, Power BI, QlikView
  • Machine Learning Frameworks: TensorFlow, PyTorch
  • Big Data Technologies: Hadoop, Spark, Hive

It’s divided into modules covering data basics, visualization, statistics, and hands-on projects with real data.

Graduates can pursue roles like Data Analyst, Business Analyst, or Market Research Analyst in various industries.

Skills You Will Gain

SQL

Variables

SQL Functions

Python

Statistics

Python Wizardry

Probability

Regression

Data Storytelling

Numpy

Visualization

Clustering

Certification Overview

Upon completing the course, you will receive a certification specific to that program, such as a “Certificate of Completion” or a named certification related to the course content.

A certification demonstrates your expertise and credibility in the field of data analytics, enhancing your career prospects and job opportunities. It provides validation of your skills to potential employers and clients.

Topics typically include data collection, cleaning, and preprocessing, exploratory data analysis, statistical analysis, machine learning algorithms, data visualization, and tools such as Python, R, SQL, and popular data analytics libraries and frameworks.

You can showcase the certification on your resume and LinkedIn profile to highlight your skills and qualifications, demonstrating your commitment to professional development.

The duration of a data analytics course certification can vary depending on the program’s structure and intensity. Courses may range from a few weeks to several months, with part-time or full-time options available.

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Mastering Data Analytics Course Reviews

FAQs

We accept all major credit and debit cards from leading banks. For any assistance, please contact Thinkcloudly Customer Support.

We offer a variety of ways to learn about the cloud, from quick hands-on labs to technical deep dives. You can ask our experts to help you from their industry experience if you are uncertain about which course or plan to choose.

Certainly, you can set up a free demo session, although if you’ve already viewed any sample recordings, you won’t need to look further. The enrollment process signifies a mutual commitment between you and us where you commit to be a good learner and we pledge to provide you with the best possible learning environment. A key part of your learning takes place in our sessions, which are supported by experienced instructors, dedicated Personal Learning Managers, and interactions with your peers. Get the full learning experience, and not just a demo.

You will receive access to the LMS immediately after enrolling and will have it for the rest of your life. You will have access to all previous class recordings, PPTs, PDFs, and assignments. In addition, you will have instant access to our 24×7 support team. You can start learning as soon as possible.

At Thinkcloudly, you’ll never miss a lecture! You can view the recorded session in your LMS anytime also the missed session can be attended in another live batch.

Teachers and tutors at Thinkcloudly are industry veterans with great experience.

Price: Original price was: $999.00.Current price is: $899.00.

Data Analytics Certification Program

Original price was: $999.00.Current price is: $899.00.

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