DATA ANALYST

What is Data Analyst

A data analyst is a professional who is responsible for collecting, organizing, and analyzing data to provide insights and inform decision-making. Data analysts often work in industries such as business, finance, and healthcare, and may be responsible for tasks such as:

1. 1. Collecting data:  Data analysts are often responsible for collecting data from various sources, such as databases, surveys, and social media. They may use a variety of tools and techniques to gather this data, such as SQL queries, web scraping, and API calls.


2. 2. Organizing data:  Once data has been collected, data analysts are responsible for organizing it in a way that makes it easier to analyze. This may involve cleaning and preprocessing the data to remove any errors or inconsistencies.


3. 3. Analyzing data:  The primary role of a data analyst is to analyze data to extract insights and inform decision-making. This may involve using statistical techniques to identify patterns and trends in the data, or using machine learning algorithms to make predictions.


4. 4. Visualizing data:  Data analysts often use visualization tools and techniques to represent data in a way that is easy for others to understand. This may involve creating charts, graphs, or maps to illustrate trends or patterns in the data.


5. 5. Communicating results:  Data analysts are responsible for communicating the results of their analyses to stakeholders, such as managers, executives, or clients. This may involve writing reports, presenting findings in meetings, or creating dashboards or visualizations to share with others.


6. 6. Continuously learning:  Data analysts must keep up to date with the latest tools, techniques, and best practices in data analysis. This may involve learning new programming languages or statistical software, or staying current with industry trends.

Overall, data analysts play a critical role in organizations by collecting, organizing, and analyzing data to inform decision-making. They use a variety of tools and techniques to extract insights from data and communicate their findings to stakeholders.

How to Become a Data Analyst

There are a few steps you can take to become a data analyst:

1. 1. Earn a bachelor's degree:  Many data analysts have a bachelor's degree in a field such as computer science, mathematics, statistics, or economics. A degree in one of these fields can provide a strong foundation in the skills and knowledge needed for a career in data analysis.


2. 2. Gain experience:  While a degree is important, practical experience is also essential for becoming a data analyst. You can gain experience through internships, part-time jobs, or by working on personal projects.


3. 3. Learn programming and data analysis tools:  Data analysts use a variety of tools and techniques to collect, organize, and analyze data. Some common tools and languages used in data analysis include SQL, Python, and Excel. You can learn these skills through online courses, bootcamps, or self-study.


4. 4. Build a portfolio:  As you gain experience and learn new skills, it's important to build a portfolio of work that you can show to potential employers. This can include projects you have completed, data visualizations you have created, or reports you have written.


5. 5. Get certified:  While not required, earning a certification in a field such as data analysis or data science can help to demonstrate your expertise and make you more competitive in the job market. There are a number of certifications available, such as the Certified Data Professional (CDP) or the Data Science Certificate from the Institute for Operations Research and the Management Sciences (INFORMS).

Overall, becoming a data analyst requires a combination of education, experience, and skills development. By earning a degree, gaining practical experience, learning relevant tools and techniques, building a portfolio, and possibly earning a certification, you can set yourself up for a successful career as a data analyst.

Career Opportunity After Data Analyst

After becoming a data analyst, there are a number of career paths you could pursue. Some potential options include:

1. 1. Data scientist:  A data scientist is a professional who combines expertise in programming, statistics, and domain knowledge to extract insights and inform decision-making. Data scientists often have a more advanced skill set than data analysts, and may be responsible for tasks such as developing machine learning models and conducting advanced statistical analyses.


2. 2. Business analyst:  A business analyst is a professional who uses data and analysis to inform business decisions. Business analysts often work in industries such as finance, healthcare, and retail, and may be responsible for tasks such as identifying trends and patterns in data, developing business plans, and recommending strategic actions.


3. 3. Data engineer:  A data engineer is a professional who is responsible for building and maintaining the infrastructure and systems that enable data analysis. Data engineers may be responsible for tasks such as designing and implementing data pipelines, building data lakes and warehouses, and optimizing data processing systems.


4. 4. Machine learning engineer:  A machine learning engineer is a professional who is responsible for designing, developing, and deploying machine learning models. Machine learning engineers often have a strong background in programming, statistics, and machine learning, and may be responsible for tasks such as developing and testing algorithms, integrating machine learning models into production systems, and optimizing model performance.


5. 5. Product manager:  A product manager is a professional who is responsible for defining and executing the strategy for a product or product line. Product managers often use data analysis to inform their decisions, and may be responsible for tasks such as defining product roadmap, setting goals and metrics, and analysing market trends.

Overall, there are a number of career paths available to data analysts, depending on their interests and goals. Some common options include data scientist, business analyst, data engineer, machine learning engineer, and product manager.

  Get Certified

Earn your Data Analyst certificate

Our Data Analyst is exhaustive and this Certificate is proof that you have taken a big leap in mastering the domain.

Differentiate Yourself with a Master's Certificate

The knowledge and Data Analyst skills you've gained working on projects, simulations, case studies will set you ahead of the competition.

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Course Syllabus

• Syllabus
• Duration
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• •   INTRODUCTION TO PYTHON
  • WHY PYTHON
  • APPLICATION AREAS OF PYTHON
  • PYTHON IMPLEMENTATIONS
  • CPYTHON
  • JYTHON
  • IRONPYTHON
  • PYPY
  • PYTHON VERSIONS
  • INSTALLING PYTHON
  • PYTHON INTERPRETER ARCHITECTURE
  • PYTHON BYTE CODE COMPILER
  • PYTHON VIRTUAL MACHINE(PVM)
• •   WRITING AND EXECUTING FIRST PYTHON PROGRAM
  • USING INTERACTIVE MODE
  • USING SCRIPT MODE
  • GENERAL TEXT EDITOR AND COMMAND WINDOW
  • IDLE EDITOR AND IDLE SHELL
  • UNDERSTANDING PRINT() FUNCTION
  • HOW TO COMPILE PYTHON PROGRAM EXPLICITLY
• •   PYTHON LANGUAGE FUNDAMENTALS
  • CHARACTER SET
  • KEYWORDS
  • COMMENTS
  • VARIABLES
  • LITERALS
  • OPERATORS
  • READING INPUT FROM CONSOLE
  • PARSING STRING TO INT, FLOAT
• •   PYTHON CONDITIONAL STATEMENTS
  • IF STATEMENT
  • IF ELSE STATEMENT
  • IF ELIF STATEMENT
  • IF ELIF ELSE STATEMENT
  • NESTED IF STATEMENT
• •   LOOPING STATEMENTS
  • WHILE LOOP
  • FOR LOOP
  • NESTED LOOPS
  • PASS, BREAK AND CONTINUE KEYWORDS
• •   STANDARD DATA TYPES
  • INT, FLOAT, COMPLEX, BOOL, NONETYPE
  • STR, LIST, TUPLE, RANGE
  • DICT, SET, FROZENSET
• •   STRING HANDLING
  • WHAT IS STRING
  • STRING REPRESENTATIONS
  • UNICODE STRING
  • STRING FUNCTIONS, METHODS
  • STRING INDEXING AND SLICING
  • STRING FORMATTING
• •   PYTHON LIST
  • CREATING AND ACCESSING LISTS
  • INDEXING AND SLICING LISTS
  • LIST METHODS
  • NESTED LISTS
  • LIST COMPREHENSION
• •   PYTHON TUPLE
  • CREATING TUPLE
  • ACCESSING TUPLE
  • IMMUTABILITY OF TUPLE
• •   PYTHON SET
  • HOW TO CREATE A SET
  • ITERATION OVER SETS
  • PYTHON SET METHODS
  • PYTHON FROZENSET
• •   PYTHON DICTIONARY
  • CREATING A DICTIONARY
  • DICTIONARY METHODS
  • ACCESSING VALUES FROM DICTIONARY
  • UPDATING DICTIONARY
  • ITERATING DICTIONARY
  • DICTIONARY COMPREHENSION
• •   PYTHON FUNCTIONS
  • DEFINING A FUNCTION
  • CALLING A FUNCTION
  • TYPES OF FUNCTIONS
  • FUNCTION ARGUMENTS
  • POSITIONAL ARGUMENTS, KEYWORD ARGUMENTS
  • DEFAULT ARGUMENTS, NON-DEFAULT ARGUMENTS
  • ARBITRARY ARGUMENTS, KEYWORD ARBITRARY ARGUMENTS
  • FUNCTION RETURN STATEMENT
  • NESTED FUNCTION
  • FUNCTION AS ARGUMENT
  • FUNCTION AS RETURN STATEMENT
  • DECORATOR FUNCTION
  • CLOSURE
  • MAP(), FILTER(), REDUCE(), ANY() FUNCTIONS
  • ANONYMOUS OR LAMBDA FUNCTION
• •   MODULES & PACKAGES
  • WHY MODULES
  • SCRIPT V/S MODULE
  • IMPORTING MODULE
  • STANDARD V/S THIRD PARTY MODULES
  • WHY PACKAGES
  • UNDERSTANDING PIP UTILITY
• •   FILE I/O
  • INTRODUCTION TO FILE HANDLING
  • FILE MODES
  • FUNCTIONS AND METHODS RELATED TO FILE HANDLING
  • UNDERSTANDING WITH BLOCK
• •   SQL REGULAR EXPRESSIONS(REGEX)
  • NEED OF REGULAR EXPRESSIONS
  • RE MODULE
  • FUNCTIONS /METHODS RELATED TO REGEX
  • META CHARACTERS & SPECIAL SEQUENCES
• •   INTRODUCTION TO DATABASE
  • DATABASE CONCEPTS
  • WHAT IS DATABASE PACKAGE?
  • UNDERSTANDING DATA STORAGE
  • RELATIONAL DATABASE (RDBMS) CONCEPT
• •   SQL (STRUCTURED QUERY LANGUAGE)
  • LSQL BASICS
  • DML, DDL & DQL
  • DDL: CREATE, ALTER, DROP
  • SQL CONSTRAINTS: NOT NULL, UNIQUE, PRIMARY & FOREIGN KEY, COMPOSITE KEY CHECK, DEFAULT
  • DML: INSERT, UPDATE, DELETE AND MERGE
  • DQL : SELECT
  • SELECT DISTINCT
  • SQL WHERE
  • SQL OPERATORS
  • SQL LIKE
  • SQL ORDER BY
  • SQL ALIASES
  • SQL VIEWS
  • SQL JOINS
  • INNER JOIN
  • LEFT (OUTER) JOIN
  • RIGHT (OUTER) JOIN
  • FULL (OUTER) JOIN
  • MY SQL FUNCTIONS
  • STRING FUNCTIONS
  • CHAR_LENGTH
  • CONCAT
  • LOWER
  • REVERSE
  • UPPER
  • NUMERIC FUNCTIONS
  • MAX, MIN, SUM
  • AVG, COUNT, ABS
  • DATE FUNCTIONS
  • CURDATE
  • CURTIME
  • NOW
• •   NUMPY PACKAGE
  • DIFFERENCE BETWEEN LIST AND NUMPY ARRAY
  • VECTOR AND MATRIX OPERATIONS
  • ARRAY INDEXING AND SLICING
• •   INTRODUCTION TO PANDAS
  • LABELED AND STRUCTURED DATA
  • SERIES AND DATAFRAME OBJECTS
• •   PANDAS PACKAGE
  • HOW TO LOAD DATASETS
  • FROM EXCEL
  • FROM CSV
  • FROM HTML TABLE
• •   ACCESSING DATA FROM DATA FRAME
  • AT & IAT
  • LOC & ILOC
  • HEAD() & TAIL()
• •   EXPLORATORY DATA ANALYSIS (EDA)
  • DESCRIBE()
  • GROUPBY()
  • CROSSTAB()
  • BOOLEAN SLICING / QUERY()
  • DATA MANIPULATION & CLEANING
  • MAP(), APPLY()
  • COMBINING DATA FRAMES
  • ADDING/REMOVING ROWS & COLUMNS
  • SORTING DATA
  • HANDLING MISSING VALUES
  • HANDLING DUPLICACY
  • HANDLING DATA ERROR
• •   DATA VISUALIZATION USING MATPLOTLIB AND SEABORN PACKAGES
  • LSCATTER PLOT, LINEPLOT, BAR PLOT
  • HISTOGRAM, PIE CHART
  • JOINTPLOT, PAIRPLOT, HEATMAP
  • OUTLIER DETECTION USING BOXPLOT
• •   ARRAY
  • DYNAMIC ARRAY, ARRAY FUNCTION, MONTH NAMES, SIZE OF AN ARRAY.
  • FUNCTION AND SUB: USER DEFINED FUNCTION, CUSTOM AVERAGE FUNCTION, VOLATILE FUNCTIONS, BYREF AND BYVAL.
  • APPLICATION OBJECT: STATUS BAR, READ DATA FROM TEXT FILE, WRITE DATA TO TEXT FILE.
  • ACTIVEX CONTROLS: TEXT BOX, LIST BOX, COMBO BOX, CHECK BOX, OPTION BUTTONS, SPIN
  • BUTTON, LOAN CALCULATOR.
• •   USER FORM
  • USER FORM AND RANGES, CURRENCY CONVERTER, PROGRESS INDICATOR, MULTIPLE
  • LIST BOX SELECTIONS, MULTICOLUMN COMBO BOX, DEPENDENT COMBO BOXES
  • LOOP THROUGH CONTROLS, CONTROLS COLLECTION
• •   POWER-BI
  • POWER-BI – HOME
  • POWER-BI – OVERVIEW
  • POWER-BI - ENVIRONMENT SETUP
  • POWER-BI - GET STARTED
  • POWER-BI – NAVIGATION
  • POWER-BI - DESIGN FLOW
  • POWER-BI - FILE TYPES
  • POWER-BI - DATA TYPES
  • POWER-BI - SHOW ME
  • POWER-BI - DATA TERMINOLOGY
• •   POWER-BI - DATA SOURCES
  • POWER-BI - CUSTOM DATA VIEW
  • POWER-BI - DATA SOURCES
  • POWER-BI - EXTRACTING DATA
  • POWER-BI - FIELDS OPERATIONS
  • POWER-BI - EDITING METADATA
  • POWER-BI - DATA JOINING
  • POWER-BI - DATA BLENDING
• •   POWER-BI – WORK SHEET
  • POWER-BI - ADD WORKSHEETS
  • POWER-BI - RENAME WORKSHEET
  • POWER-BI - SAVE & DELETE WORKSHEET
  • POWER-BI - REORDER WORKSHEET
  • POWER-BI - PAGED WORKBOOK
• •   POWER-BI – CALCULATION
  • POWER-BI – OPERATORS
  • POWER-BI – FUNCTIONS
  • POWER-BI - NUMERIC CALCULATIONS
  • POWER-BI - STRING CALCULATIONS
  • POWER-BI - DATE CALCULATIONS
  • POWER-BI - TABLE CALCULATIONS
  • POWER-BI - LOD EXPRESSIONS
• •   POWER-BI – SORTING & FILTER
  • POWER-BI - BASIC SORTING
  • POWER-BI - BASIC FILTERS
  • POWER-BI - QUICK FILTERS
  • POWER-BI - CONTEXT FILTERS
  • POWER-BI - CONDITION FILTERS
  • POWER-BI - TOP FILTERS
  • POWER-BI - FILTER OPERATIONS
• •   POWER-BI – CHARTS
  • POWER-BI - BAR CHART
  • POWER-BI - LINE CHART
  • POWER-BI - PIE CHART
  • POWER-BI – CROSSTAB
  • POWER-BI - SCATTER PLOT
  • POWER-BI - BUBBLE CHART
  • POWER-BI - BULLET GRAPH
  • POWER-BI - BOX PLOT
  • POWER-BI - TREE MAP
  • POWER-BI - BUMP CHART
  • POWER-BI - GANTT CHART
  • POWER-BI – HISTOGRAM
  • POWER-BI - MOTION CHARTS
  • POWER-BI - WATERFALL CHARTS
  • POWER-BI – DASHBOARD
• •   PROJECTS
  • ONE PROJECT USING PYTHON & SQL
  • ONE DASHBOARD USING POWER-BI
• •   DATA ANALYSIS – VISUALIZATION USING PYTHON
  • INTRODUCTION EXPLORATORY DATA ANALYSIS
  • DESCRIPTIVE STATISTICS, FREQUENCY TABLES, AND SUMMARIZATION
  • UNIVARIATE ANALYSIS (DISTRIBUTION OF DATA & GRAPHICAL ANALYSIS)
  • BIVARIATE ANALYSIS(CROSS TABS, DISTRIBUTIONS & RELATIONSHIPS, GRAPHICAL ANALYSIS)
  • CREATING GRAPHS- BAR/PIE/LINE CHART/HISTOGRAM/ BOXPLOT/ SCATTER/ DENSITY ETC)
  • IMPORTANT PACKAGES FOR EXPLORATORY ANALYSIS(NUMPY ARRAYS, MATPLOTLIB, SEABORN, PANDAS ETC)
• •   IMPORTING AND EXPORTING DATA USING PANDAS
  • IMPORTING DATA FROM VARIOUS SOURCES (CSV, TXT, EXCEL, ACCESS, ETC)
  • DATABASE INPUT (CONNECTING TO THE DATABASE)
  • VIEWING DATA OBJECTS – SUBSETTING, METHODS
  • EXPORTING DATA TO VARIOUS FORMATS
  • IMPORTANT PYTHON FUNCTIONS: PANDAS
• •   DATA ANALYTICS WITH PYTHON
  • COURSE INTRODUCTION
  • DATA ANALYTICS OVERVIEW
  • STATISTICAL COMPUTING
  • MATHEMATICAL COMPUTING USING NUMPY
  • DATA MANIPULATION WITH PANDAS
  • DATA VISUALIZATION WITH PYTHON
  • INTRO TO MODEL BUILDING

TYPE	DURATION	HOURS	TRAINING MODE
Regular Track	45-60 Days	2 Hours a Day	Live Classroom
Weekend Track	8 Weekends	3 Hours a Day	Live Classroom
Fast Track	7 Days	6+ Hours a Day	Live Classroom

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