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Why Phinco Elite

Benefits of Joining Phinco Elite

Dedicated Mentors Team

Our dedicated team of mentors will support you throughout your journey, seeking feedback to improve your learning experience with daily support.

Career Services Team

We provide assistance with resume preparation, LinkedIn optimization, mock interviews, and access to an interview question bank to support you throughout the interview process.

Real Capstone Projects and Case Studies

During training,you will work on real capstone projects and case students so that you can get real industry relevant experience and you will be job ready.

Immersive Learning

We offer interactive courses in a small class setting to provide personalized attention, ensuring a high-quality learning experience.

Learning Management System(LMS)

PHINCO ELITE offers a software platform with an LMS for learners, allowing them to access recorded sessions, Quizzes, tests, content for lifetime.

Foster Alumni Connections

PHINCO ELITE has a strong alumni network of 6000+ professionals across various organizations, supported by a networking application for alumni connections.

PHINCO ELITE’s Professional services to enhance your Career

Acquire knowledge on multiple digital cloud based platforms with top-notch material, interactive sessions and guidance from renowned professionals in the field to reach your intended objective

Tools & Technologies

Who Is This Course For?

Final year Grads/ Post Grads

Freshers

Working Professionals

No Prior Knowledge is required

Is This Course Up to Industry Standards?

Absolutely, YES! ✅ Our course is packed with industry-relevant content, interactive live sessions, hands-on projects, and case studies to build your confidence. 🌟 Many learners from diverse backgrounds have successfully transitioned to Data Science and Analytics, and their success stories are on our website. 📈

Plus, you'll get access to our exclusive job portal, PHINCO Connect, for career growth strategies, LinkedIn tips, HR round questions, mock interviews, salary negotiation tactics, and a guarantee of 5+ interviews. 🚀👔

Up skill with PHINCO ELITE

Our goal is to provide you with a comprehensive educational experience. We continue to support you when you enter the workforce with a fresh outlook thanks to our unique career support services. Gain entry to more than 100 hiring partners and discover countless opportunities.

Resume Building

Interview Question Bank

LinkedIn Enhancement

Mock Calls

5+ Assured Interviews

Curriculum

1. Introduction to Data Science and Analysis 📚
  • Overview of Data Science 🌐
  • Key Roles & Responsibilities 🧑‍💼
  • Essential Tools & Technologies 🛠️.
  • Career Path in Data Science Fields 🤑
  • Roles in Data Science Field.
Excel

Module 1: Exploring Data 🔍📊

  • Welcome to Excel: An Overview of the World’s Most Popular Spreadsheet Software 🖥️📈
  • Excel Beyond Limits 🚀📊
  • Mastering the Basics of Spreadsheet Navigation 🧭📋
  • Working on Rows and Columns 📏🔢
  • Looking for Exact Matches 🔍✔️
  • Trimming Function and Its Usage ✂️📉
  • Sorting the Data 🔢🔄
  • Nesting Functions 🏗️🔧
  • Data Types in Excel 📊🔠
  • Type Conversion in Excel 🔄🔢

Module 2: Preparing Data 🛠️📋

  • Mastering Text Functions in Excel: CONCATENATE, UPPER, LOWER, and PROPER ✍️🔤
  • Mastering Text Extraction in Excel: LEFT, RIGHT, and SUBSTITUTE Functions ✂️🔍
  • Mastering Excel’s DATE Function: Effective Techniques for Date Handling 📅🛠️
  • Excel DATEDIF Function (Between Two Dates) 📅↔️📅
  • How To Use Relative & Absolute Cell References in Excel 🔄🔒
  • Harnessing the Power of VLOOKUP for In-depth Insights 🔍🔎
  • Unleashing the Power of SUMIF Function in Excel 🔍➕

Module 3: Analyzing the Data 📈🔍

  • Mastering the COUNT, COUNTA, and COUNTBLANK Functions 🧮🔢📊
  • Unleashing the Power of COUNTIF Function in Excel 📊🔍
  • Using Excel’s Core Calculation Functions: A Practical Guide 📈🛠️
  • Excel Logic Functions Explained: IF, AND, and OR in Practice 🤔🔄
  • Organizing Data with UNIQUE and SORT Functions 🗂️🔄
SQL

Module 1: SQL Fundamentals 📚💻

  • SQL Installation & Setup 🛠️🔧
  • Types of SQL Commands 📜🗂️
  • DDL Commands for Database and Tables 🏗️📋
  • SQL Constraints 🚫📊

Module 2: Data Manipulation with SQL (DML) ✏️🔄

  • Modifying Data in Tables ✍️📋
  • Retrieving Data with SQL 🔍📊

Module 3: Intermediate SQL Queries 🔍📈

  • Selecting Columns 📋🔢
  • Filtering Rows 🔍🗂️
  • Aggregate Functions 📊➕
  • Sorting & Grouping 🔢📊
  • Null Values ❓📉
  • Date and Time Functions 📅⏰
  • Working with Expressions 🧮🔢
  • Order of Execution of SQL Commands 🔄🗂️

Module 4: Joining & Combining Data 🔗🔄

  • Types of Joins 🔍🔗
  • Left & Right Join ⬅️➡️
  • Inner Join and Full Join 🔄🔁
  • Cross Join and Self Join 🔄👤
  • Understanding Table Relationships 🗂️🔗

Module 5: Data Preprocessing & Analysis 🔧📊

  • Handling Missing Values ❓📉
  • Handling Duplicates 🔄🚫
  • Data Transformation 🔄🛠️
  • Working with Dates and Times 📅⏳
  • Data Filtering and Selection 🔍📋
  • Analyzing Time Series Data 📈⏳
  • Performance Optimization ⚡🔧
  • Working with JSON and XML Data 📄🔢
  • Data Blending 🥤🔄

Module 6: Advanced SQL 🚀📊

  • SQL Views 👁️📋
  • Triggers 🚨🔄
  • Performance Tuning ⚙️🚀
  • Backup and Recovery 💾🔄
  • Advanced Joins 🔗🔍
  • Dynamic SQL 🌀🔢
  • Materialized Views 🗂️🔍
  • Database Administration Tasks 🛠️📋

Module 7: Window Functions in SQL 🪟🔢

  • Introduction to Window Functions 📚🪟
  • Analytical Functions 🔍📈
  • Aggregating Data Using Window Functions 📊🔢
  • Partitioning Data and Applying Window Functions 📋🔄
Python

Module 1: Data Preprocessing with Google Play Store 📱🔧

  • Introduction to EDA 📊🔍
  • Data Cleaning 🧹🗃️
  • Data Visualization 📈🎨
  • Data Analysis 🔍📉

Module 2: Advanced Data Preprocessing with Google Play Store 📱🔍

  • Data Preprocessing – Removing Null Value Rows 🚫📋
  • Data Analysis – Numeric 🔢📊
  • Data Analysis – Categorical 🗂️🔍
  • Data Analysis – Automatic Categorical 🤖🗂️
  • Null Values Handling – Numeric 🔢❓
  • Null Values Handling – Categorical 🗂️❓
  • Null Values Handling Overall 🗃️❓

Module 3: Introduction to EDA 📊🔍

  • Introduction to EDA 📚🔎
  • Understanding Your Data 🤔📈

Module 4: Data Cleaning 🧹🔧

  • Dealing with Missing Values ❓🔍
  • Dealing with Duplicate Data 🔄🚫
  • Outliers 🚨📊
  • Outlier Removal Using Z-Score 🔢📉
  • Outlier Removal Using IQR 📏🔄
  • Outlier Removal Using Percentile 📈🔢
  • Correction of Data Type 🔄📊

Module 5: Data Visualization 📊🎨

  • Univariate Analysis (Non-Graphical) 📋🔍
  • Univariate Visualizations (Categorical) 🗂️📈
  • Univariate Visualizations (Numerical) 🔢📉
  • Bivariate Analysis (Numerical-Categorical) 🔢🗂️
  • Bivariate Visualizations (Categorical) 🗂️🔍
  • Bivariate Visualization (Numerical) 🔢🔍

Module 6: Data Analysis 🔍📊

  • Data Analysis with Multiple Columns 📊🔢
  • Data Analysis Using Conditions 🔄🔍
  • GroupBy in Pandas 📊🔗
Power BI

Introduction to PowerBI 📊🔍

  • Overview of PowerBI 🌐🖥️
  • PowerBI Interface Tour 🖱️📋
  • PowerBI Desktop vs. PowerBI Service 💻☁️
  • Connecting to Data Sources 🔗📊
  • Creating Your First Report 📝📊

Understanding the Parameters ⚙️📋

  • What Are Parameters? ❓🔍
  • Creating Parameters ✍️🔧
  • Using Parameters in Queries 🔄📈
  • Parameter Controls and Their Use 📊🎛️
  • Dynamic Parameter Values 🔄📉

Fundamentals of PowerBI 📚💡

  • Data Import and Transformation 🔄📥
  • Data Modeling Basics 📊🔗
  • Building and Managing Relationships 🔗🗂️
  • Introduction to DAX (Data Analysis Expressions) 🔢📏
  • Creating Basic Visualizations 📉🔧
Streamlit

Module 1: Getting Started with Streamlit 🚀🖥️

  • Introduction to Streamlit 📚🔍
  • Streamlit Setup 🛠️📥
  • Basic Output Tags 🏷️📝
  • Inspecting the Website 🔍🌐
  • Text Input 📝🔠
  • Special Input with Buttons 🔘✨
  • Forms 📝📋
  • Integrating Scripts 🔗📜

Module 2: Page Beautification 🎨🖼️

  • Working with Columns 📊🗂️
  • Working with Tabs 📑🔄
  • Expander & Empty Functionalities 🔽📂
  • Advanced Display & Progress Options 📈🔧
  • Echo and Stop Commands 🔄⏹️

Module 3: Working with Data 📊🔗

  • Working with Media Files 🎥📁
  • DataFrames with Streamlit 🗃️📊
  • File Uploading ⬆️📂
  • Image Converter 🖼️🔄
  • Image Rotation 🔄🖼️

Module 4: Introduction to Data Visualization 📈🔍

  • Getting Started with Basic Plots with Streamlit 📊🔧
  • Plots with Matplotlib and Seaborn 📉🎨
  • Visualization with Plotly 📊🔍

Module 5: Deployment 🚀🌐

  • Deployment on Streamlit Server 🌐🖥️
Statistics

Introduction to Statistics 📊🔍

  • What is Statistics? ❓📚
  • Importance of Statistics 📈🔍
  • Applications of Statistics 🏥📉
  • Statistical Methods Overview 📋🛠️

Data and Their Types 📊🔢

  • Types of Data 🗃️🔠
    • Quantitative Data 🔢📏
    • Qualitative Data 🗂️🔡
  • Levels of Measurement 🎚️🔢
    • Nominal 🏷️
    • Ordinal 📈
    • Interval 📅
    • Ratio 🔢📏

Introduction to Descriptive Stats 📊📋

  • What is Descriptive Statistics? 📈🔍
  • Purpose of Descriptive Stats 🎯🔍
  • Types of Descriptive Statistics 📊🔢

Measures of Frequency 📊🔢

  • Frequency Distribution 📉📋
  • Frequency Tables 📊🗂️
  • Histograms 📈🔢
  • Frequency Polygons 📉🔗

Measures of Central Tendency 🧮📈

  • Mean ➗🔢
  • Median 📍🔢
  • Mode 🔁🔢
  • Comparing Measures of Central Tendency 🔄📈

Measures of Dispersion 📏📊

  • Range 🏆🔢
  • Variance 📈🔢
  • Standard Deviation 📊🔢
  • Interquartile Range (IQR) 📉🔢

Measures of Shape 📏📊

  • Skewness 🏞️🔢
  • Kurtosis 🏔️🔢
  • Symmetry of Distribution 🔄📈
Specializations
  • Machine Learning Introduction 🤖📚
  • Machine Learning Advanced 🚀📊
  • Introduction to AI and Deep Learning 🤖🔍
  • Natural Language Processing 🗣️🔍
  • Generative AI 🧠🎨
  • ChatGPT 🗨️🤖

6 Months

120+ Learning Hours

10+ Tools

5+ Assured Interviews

Have questions? Contact us

FAQs

What are the prerequisites for pursuing this course?

Any one from any educational background with Zero knowledge can do this program

10+2/Diploma/Graduation/PG/Freshers/Career Gaps/Homemakers/Freelancers can enrol

What is the average salary of a Data Science Associate?

The estimated total pay for a Data Science Associate is ₹8,00,000 per year, with an average salary of ₹5,00,000 per year. This number represents the median, which is the midpoint of the ranges from our proprietary Total Pay Estimate model and based on salaries collected from our users. The estimated additional pay is ₹3,00,000 per year. Additional pay could include cash bonus, commission, tips, and profit sharing.

Which industries hire Data Science Professionals the Most?

Every industry need Data Science teams to understand their products and services .Industries include Education, retail, finance, entertainment, government and public sector, higher education, sharing economy services, sales & marketing, agriculture, business intelligence, healthcare, and Banking,Media etc.,

How can i resolve my doubts while learning

We have a team called students mentors team who will be with you from the day of your joining to till you complete the program.You can reach them through our Portal SLACK

How can I get Career Support After course Completion?

Career Services Team will help you with Resume Building,Mock Calls,Interview Question Bank and we will also a Provide Access to our job Portal PHINCO ELITE JOBS where you can access unlimited Job openings

I have no tech skills.Will it work for me?

We teach from scratch. So we will cover everything from Basics to Advanced Concepts with Hands-on Experience.

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