DATA SCIENCE

What is Data Science

Data science is a field that involves using statistical and computational techniques to extract insights and knowledge from data. It encompasses a wide range of activities, including data cleaning and preparation, data visualization, machine learning, and statistical analysis.

Data scientists often work with large and complex datasets, and they use a variety of tools and techniques to analyze and interpret this data. Some of the key skills that a data scientist might have include programming, statistics, data visualization, machine learning, and domain expertise in a particular area.

Data science is an interdisciplinary field that draws from a range of disciplines, including computer science, statistics, and subject-specific expertise. It is a growing field, with the increasing availability of data and the need for businesses and organizations to make data-driven decisions driving demand for data scientists.

Scope After Data Science

The scope of data science is quite broad and encompasses a range of activities, including:

1. 1. Data collection:  This involves gathering data from a variety of sources, such as databases, surveys, and online platforms.


2. 2. Data cleaning and preparation:  This involves cleaning and preparing data for analysis, which can involve tasks such as filling in missing values, removing outliers, and converting data into a format that is suitable for analysis.


3. 3. Data visualization:  This involves using visualizations to explore and understand data, and to communicate findings to others.


4. 4. Machine learning:  This involves using algorithms to enable computers to learn from data and improve their performance over time.


5. 5. Statistical analysis:  This involves using statistical techniques to understand patterns and relationships in data.


6. 6. Data-driven decision making:  This involves using data and insights from data analysis to inform business and organizational decision making.

Overall, the scope of data science is broad and encompasses a wide range of activities that are focused on extracting insights and knowledge from data. It is a rapidly growing field, with demand for data scientists continuing to increase as more organizations seek to make data-driven decisions.

Key Features of Data science

There are a number of key features of data science that distinguish it from other fields:

1. 1. Interdisciplinary:  Data science draws from a range of disciplines, including computer science, statistics, and subject-specific expertise, and it often requires a combination of technical and domain-specific skills.


2. 2. Data-driven:  Data science is focused on using data and statistical analysis to extract insights and inform decision making.


3. 3. Computational:  Data science often involves the use of advanced computational techniques and tools, such as machine learning algorithms and high-performance computing resources.


4. 4. Problem-solving:  Data science involves using data and analytical approaches to solve a wide range of problems, from understanding consumer behavior to optimizing business processes.


5. 5. Dynamic:  The field of data science is rapidly evolving, with new technologies and techniques being developed all the time. This means that data scientists need to be comfortable with continuously learning and adapting to new developments in the field.

Overall, data science is a complex and multifaceted field that involves using a range of technical and analytical skills to extract insights and knowledge from data.

Career After Data science

There are many career opportunities available for individuals with skills in data science, which involves using statistical and computational techniques to extract insights and knowledge from data. Some examples of roles that may be open to individuals with data science skills include:

1. 1. Data Scientist:  Data scientists use a range of tools and techniques to analyse data, often with a focus on using machine learning to identify patterns and trends.


2. 2. Business Intelligence Analyst:  Business intelligence analysts use data analysis to inform business decision making and strategy.


3. 3. Data Engineer:  Data engineers build and maintain the infrastructure and systems that are used to store, process, and analyse data.


4. 4. Statistician:  Statisticians use statistical techniques to analyse data and inform decision making in a variety of fields.


5. 5. Data Journalist:  Data journalists use data analysis to tell stories and inform the public about important issues.


6. 6. Data Product Manager:  Data product managers are responsible for developing and launching data-driven products, and may work closely with data scientists to define product requirements and analyse data to inform product development.


7. 7. Data Consultant:  Data consultants help organizations to understand and use their data effectively, and may work with clients to identify areas where data analysis can drive business value.

Overall, there are many career opportunities available for individuals with skills in data science, as the ability to extract insights from data is highly valued in a wide range of industries.

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Our Data Science Program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.

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Course Syllabus

• Syllabus
• Duration
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• •   MODULE 1: INTRODUCTION TO DATA SCIENCE
  • SELECTING ROWS/OBSERVATIONS
  • ROUNDING NUMBER
  • SELECTING COLUMNS/FIELDS
  • MERGING DATA
  • DATA AGGREGATION
  • DATA MUNGING TECHNIQUES
• •   MODULE 2: INTRODUCTION TO PYTHON
  • WHAT IS PYTHON?
  • WHY PYTHON?
  • INSTALLING PYTHON
  • PYTHON IDES
  • JUPYTER NOTEBOOK OVERVIEW
• •   MODULE 3: PYTHON BASICS
  • PYTHON BASIC DATA TYPES
  • LISTS
  • SLICING
  • IF STATEMENTS
  • LOOPS
  • DICTIONARIES
  • TUPLES
  • FUNCTIONS
  • ARRAY
• •   MODULE 4: PYTHON PACKAGES
  • PANDAS
  • NUMPY
  • SCI-KIT LEARN
  • MAT-PLOT LIBRARY
• •   MODULE 5: IMPORTING DATA
  • READING CSV FILES
  • SAVING IN PYTHON DATA
  • LOADING PYTHON DATA OBJECTS
  • WRITING DATA TO CSV FILE
• •   MODULE 6: MANIPULATING DATA
  • SELECTING ROWS/OBSERVATIONS
  • ROUNDING NUMBER
  • SELECTING COLUMNS/FIELDS
  • MERGING DATA
  • DATA AGGREGATION
  • DATA MUNGING TECHNIQUES
• •   MODULE 1: MACHINE LEARNING- SUPERVISED LEARNING
  • LINEAR REGRESSION
  • LINEAR EQUATION
  • SLOPE
  • INTERCEPT
  • R SQUARE VALUE
  • LOGISTIC REGRESSION
  • ODDS RATIO
  • PROBABILITY OF SUCCESS
  • PROBABILITY OF FAILURE BIAS VARIANCE TRADEOFF
  • ROC CURVE
  • BIAS VARIANCE TRADEOFF
• •   HANDS-ON-EXERCISE:
  • WE’VE REVIEWED THE MAIN WAYS TO APPROACH THE PROBLEM OF MODELING DATA USING SIMPLE AND DEFINITE
• •   MODULE 2: MACHINE LEARNING- UNSUPERVISED LEARNING
  • K-MEANS
  • K-MEANS ++
  • HIERARCHICAL CLUSTERING
• •   MODULE 3: MACHINE LEARNING- SVM
  • SUPPORT VECTORS
  • HYPERPLANES
  • 2-D CASE
  • LINEAR HYPERPLANE
• •   MODULE 4: MACHINE LEARNING- SVM KERNAL
  • LINEAR
  • RADIAL
  • POLYNOMIAL
• •   MODULE 5: MACHINE LEARNING- OTHER MACHINE LEARNING ALGORITHMS
  • K – NEAREST NEIGHBOUR
  • NAÏVE BAYES CLASSIFIER
  • DECISION TREE – CART
  • DECISION TREE – C50
  • RANDOM FOREST
• •   HANDS-ON-EXERCISE:
  • WE HAVE COVERED THE SIMPLEST BUT STILL VERY PRACTICAL MACHINE LEARNING MODELS IN AN EMINENTLY PRACTICAL WAY TO GET US STARTED ON THE COMPLEXITY
  • WHERE WE WILL COVER SEVERAL REGRESSION TECHNIQUES, IT WILL BE TIME TO GO AND SOLVE A NEW TYPE OF PROBLEM THAT WE HAVE NOT WORKED ON, EVEN IF IT’S POSSIBLE TO SOLVE THE PROBLEM WITH CLUSTERING METHODS (REGRESSION), USING NEW MATHEMATICAL TOOLS FOR APPROXIMATING UNKNOWN VALUES.
  • IN IT, WE WILL MODEL PAST DATA USING MATHEMATICAL FUNCTIONS, AND TRY TO MODEL NEW OUTPUT BASED ON THOSE MODELING
• •   MODULE 1: DEEP LEARNING ALGORITHMS
  • CNN – CONVOLUTIONAL NEURAL NETWORK
  • RNN – RECURRENT NEURAL NETWORK
  • ANN – ARTIFICIAL NEURAL NETWORK
• •   MODULE 2: DEEP LEARNING- INTRODUCTION TO NLP
  • TEXT PRE-PROCESSING
  • NOISE REMOVAL
  • LEXICON NORMALIZATION
  • LEMMATIZATION
  • STEMMING
  • OBJECT STANDARDIZATION
• •   MODULE 3: DEEP LEARNING- TEXT TO FEATURES(FEATURE ENGINEERING)
  • SYNTACTICAL PARSING
  • DEPENDENCY GRAMMAR
  • PART OF SPEECH TAGGING
  • ENTITY PARSING
  • NAMED ENTITY RECOGNITION
  • TOPIC MODELLING
  • N-GRAMS
  • TF – IDF
  • FREQUENCY / DENSITY FEATURES
  • WORD EMBEDDING’S
• •   MODULE 4: DEEP LEARNING- TASKS OF NLP
  • TEXT CLASSIFICATION
  • TEXT MATCHING
  • LEVENSHTEIN DISTANCE
  • PHONETIC MATCHING
  • FLEXIBLE STRING MATCHING
• •   MODULE 1: POWER-BI COURSE MATERIAL
  • START PAGE
  • SHOW ME
  • CONNECTING TO EXCEL FILES
  • CONNECTING TO TEXT FILES
  • CONNECT TO MICROSOFT SQL SERVER
  • CONNECTING TO MICROSOFT ANALYSIS SERVICES
  • CREATING AND REMOVING HIERARCHIES
  • BINS
  • JOINING TABLES
  • DATA BLENDING
• •   MODULE 2: LEARN POWER-BI BASIC REPORTS
  • ARAMETERS
  • GROUPING EXAMPLE 1
  • GROUPING EXAMPLE 2
  • EDIT GROUPS
  • SET
  • COMBINED SETS
  • CREATING A FIRST REPORT
  • DATA LABELS
  • CREATE FOLDERS
  • SORTING DATA
  • ADD TOTALS, SUBTOTALS AND GRAND TOTALS TO REPORT
• •   HANDS-ON-EXERCISE
  • INSTALL POWER-BI DESKTOP
  • CONNECT POWER-BI TO VARIOUS DATASETS: EXCEL AND CSV FILES
• •   MODULE 3: LEARN POWER-BI CHARTS
  • AREA CHART
  • BAR CHART
  • BOX PLOT
  • BUBBLE CHART
  • BUMP CHART
  • BULLET GRAPH
  • CIRCLE VIEWS
  • DUAL COMBINATION CHART
  • DUAL LINES CHART
  • FUNNEL CHART
  • TRADITIONAL FUNNEL CHARTS
  • GANTT CHART
  • GROUPED BAR OR SIDE BY SIDE BARS CHART
  • HEATMAP
  • HIGHLIGHT TABLE
  • HISTOGRAM
  • CUMULATIVE HISTOGRAM
  • LINE CHART
  • LOLLIPOP CHART
  • PARETO CHART
  • PIE CHART
  • SCATTER PLOT
  • STACKED BAR CHART
  • TEXT LABEL
  • TREE MAP
  • WORD CLOUD
  • WATERFALL CHART
• •   HANDS-ON-EXERCISE
  • CREATE AND USE STATIC SETS
  • CREATE AND USE DYNAMIC SETS
  • COMBINE SETS INTO MORE SETS
  • USE SETS AS FILTERS
  • CREATE SETS VIA FORMULAS
  • CONTROL SETS WITH PARAMETERS
  • CONTROL REFERENCE LINES WITH PARAMETERS
• •   MODULE 4: LEARN POWER-BI ADVANCED REPORTS
  • DUAL AXIS REPORTS
  • BLENDED AXIS
  • INDIVIDUAL AXIS
  • ADD REFERENCE LINES
  • REFERENCE BANDS
  • REFERENCE DISTRIBUTIONS
  • BASIC MAPS
  • SYMBOL MAP
  • USE GOOGLE MAPS
  • MAPBOX MAPS AS A BACKGROUND MAP
  • WMS SERVER MAP AS A BACKGROUND MAP
• •   HANDS-ON-EXERCISE
  • CREATE BARCHARTS
  • CREATE AREA CHARTS
  • CREATE MAPS
  • CREATE INTERACTIVE DASHBOARDS
  • CREATE STORYLINES
  • UNDERSTAND TYPES OF JOINS AND HOW THEY WORK
  • WORK WITH DATA BLENDING IN POWER-BI
  • CREATE TABLE CALCULATIONS
  • WORK WITH PARAMETERS
  • CREATE DUAL AXIS CHARTS
  • CREATE CALCULATED FIELDS
• •   MODULE 5: LEARN POWER-BI CALCULATIONS & FILTERS
  • CALCULATED FIELDS
  • BASIC APPROACH TO CALCULATE RANK
  • ADVANCED APPROACH TO CALCULATE RA
  • CALCULATING RUNNING TOTAL
  • FILTERS INTRODUCTION
  • QUICK FILTERS
  • FILTERS ON DIMENSIONS
  • CONDITIONAL FILTERS
  • TOP AND BOTTOM FILTERS
  • FILTERS ON MEASURES
  • CONTEXT FILTERS
  • SLICING FLITERS
  • DATA SOURCE FILTERS
  • EXTRACT FILTERS
• •   HANDS-ON-EXERCISE
  • CREATING DATA EXTRACTS IN POWER-BI
  • UNDERSTAND AGGREGATION, GRANULARITY, AND LEVEL OF DETAIL
  • ADDING FILTERS AND QUICK FILTERS
• •   MODULE 6: LEARN POWER-BI DASHBOARDS
  • CREATE A DASHBOARD
  • FORMAT DASHBOARD LAYOUT
  • CREATE A DEVICE PREVIEW OF A DASHBOARD
  • CREATE FILTERS ON DASHBOARD
  • DASHBOARD OBJECTS
  • CREATE A STORY
• •   MODULE 7: SERVER
  • POWER-BI ONLINE.
  • OVERVIEW OF POWER-BI
  • PUBLISHING POWER-BI OBJECTS AND SCHEDULING/SUBSCRIPTION.

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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