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Data Analytics

NexTech Skills facilitates real and functional training that enhances your ability to utilize this operating system confidently as your guide to enhancing your career

Duration: 3 Months

Program Fee: PKR.14,000

Fee Installment Plan: Available

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OVERVIEW

The Data Analytics Mastery Course (4 Months) is designed to provide students with a comprehensive understanding of data analysis techniques and tools, enabling them to turn raw data into actionable insights. This course covers key topics such as data cleaning, data visualization, statistical analysis, and predictive modeling. Students will learn how to use industry-standard tools such as Excel, Python, SQL, and Tableau to process, analyze, and present data. By the end of the course, students will be equipped with the skills needed to perform data-driven decision-making and drive business outcomes.

1. Introduction to Data Analytics
  • What is Data Analytics?: Understanding the importance and applications of data analytics in different industries.
  • Data Analytics Process: The steps involved in data analytics: collecting, cleaning, analyzing, and visualizing data.
  • Types of Data Analytics: Descriptive, diagnostic, predictive, and prescriptive analytics.
  • Role of Data Analyst: Exploring the responsibilities of a data analyst and the skills required in the industry.
  • Data Sources and Types: Identifying different sources of data, including structured, unstructured, and semi-structured data.
  • Data Collection Techniques: Gathering data from databases, APIs, and data scraping methods.
  • Data Cleaning: Techniques for handling missing data, removing duplicates, correcting errors, and dealing with outliers.
  • Data Transformation: Converting raw data into a format suitable for analysis, including normalization and scaling.
  • Excel Fundamentals: Mastering Excel functions and formulas for data analysis (VLOOKUP, PivotTables, INDEX-MATCH, etc.).
  • Data Analysis with Excel Tools: Using Excel’s built-in tools like Power Query, Power Pivot, and Solver for deeper analysis.
  • Data Visualization in Excel: Creating charts, graphs, and dashboards to visualize data and communicate insights effectively.
  • Advanced Excel Techniques: Applying advanced Excel features for data analysis, including scenario analysis, goal seek, and data modeling.
  • SQL Fundamentals: Understanding SQL syntax and structure, including SELECT, INSERT, UPDATE, DELETE commands.
  • Database Queries: Writing complex queries to retrieve and manipulate data from relational databases.
  • Joins and Subqueries: Using inner joins, outer joins, and subqueries to combine data from multiple tables.
  • SQL for Data Analysis: Aggregating, filtering, and sorting data to extract insights using GROUP BY, HAVING, and WHERE clauses.
  • Tableau Fundamentals: Introduction to Tableau for creating interactive data visualizations.
  • Building Dashboards: How to create dashboards to showcase key metrics and insights.
  • Charts and Graphs in Tableau: Mastering different types of visualizations such as bar charts, line graphs, heat maps, and geographical maps.
  • Advanced Tableau Techniques: Creating calculated fields, parameters, and using Tableau’s advanced features for dynamic visualization.
  • Python Fundamentals: Learning the basics of Python programming, including variables, data types, loops, and functions.
  • Python Libraries for Data Analysis: Introduction to essential libraries like Pandas, NumPy, and Matplotlib for data manipulation and visualization.
  • Data Cleaning in Python: Using Pandas to clean and preprocess data, handling missing values, and performing data transformations.
  • Data Visualization in Python: Creating visualizations using libraries like Matplotlib and Seaborn.
  • Basic Statistics for Data Analytics: Understanding descriptive statistics such as mean, median, mode, variance, and standard deviation.
  • Probability Distributions: Learning about different probability distributions and their application in data analysis.
  • Hypothesis Testing: Performing hypothesis testing to make data-driven decisions using t-tests, chi-square tests, and ANOVA.
  • Correlation and Regression: Understanding correlation, linear regression, and how they are used to identify relationships between variables.
  • Introduction to Predictive Analytics: Understanding how to use historical data to predict future outcomes.
  • Basic Machine Learning Models: Introduction to supervised and unsupervised learning algorithms like linear regression, decision trees, and clustering.
  • Model Evaluation and Selection: Techniques for evaluating the performance of machine learning models (e.g., accuracy, precision, recall, F1-score).
  • Hands-On Project: Building a simple machine learning model using Python.
  • Time Series Analysis: Analyzing time-dependent data and making forecasts using time series methods.
  • Text Analytics: Introduction to natural language processing (NLP) for analyzing textual data (sentiment analysis, word frequency, etc.).
  • Big Data Analytics: Overview of big data technologies such as Hadoop and Spark for handling and analyzing large datasets.
  • Data Analytics for Business Decision-Making: How data analytics informs decision-making in areas such as marketing, sales, finance, and operations.
  • Capstone Project: Students will apply what they’ve learned by working on a real-world data analytics project. The project will include data collection, cleaning, analysis, visualization, and reporting.
  • Portfolio Development: Guidance on creating a professional portfolio showcasing completed projects, which is essential for landing jobs in data analytics.
  • Career Preparation: Resume building, interview preparation, and strategies for pursuing a career in data analytics.
  • Copying, Moving, and Deleting Files
  • Understanding File Permissions and Ownership
  • Modifying Permissions and Ownership with chmod, chown, and chgrp
  • Understanding Linux File Systems (e.g., ext4, xfs)
  • Mounting and Unmounting File Systems
  • Checking Disk Usage and Free Space (df, du)
  • Disk Partitioning and Formatting

Certification

Upon successful completion of the course and the capstone project, students will receive the Data Analytics Mastery Certification, demonstrating their ability to work with complex data sets and provide actionable insights.

Career Opportunities

Graduates of the Data Analytics Mastery Course can pursue a variety of data-related roles, including:

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