BEPEC'S ACHIEVEMENTS
Data Science masters been one of our flagship course across different branches and different places we are operating. On an average 2530 people get placed on Data science every month.
In BEPEC, nearly 200 participants work on various data science projects on python and r programming. Some data science participants work on power BI, tableau, python, R programming, openCV e.t.c. in their data science project work.
Once in a month we share success stories of our data science participants who got placed into their job roles. What was the experience while learning? How they finally made it, their challenges as non programmers.
How our data science masters program helping them to learn either via online or classroom? Stay tuned with BEPEC to know about this.
ABOUT OUR COURSE:
Data Science been one of the buzz word across the industries. Every company now a days they are switching into Data Science. By 2020 most of the companies going to work 8090% on various data science related projects. If companies are taking a step towards Data science, most of the old technology related jobs get wrapped up. Most of the companies are paying good data science salaries. Some companies call data science some companies call it like data analytics. People are having various doubts regarding data science vs data analytics. Yes, building data science related skill is bit hard, but if you are able invest at least 1 hour per day would be good for your career. Starting with data science python would be a right choice.
What is Data Science? What is Data Science course you are learning after learning data science you get placed as data scientist. What this data scientist do? Data Scientist is the person who extract information or valuable insight out of structured data like rows and columns eg: your excel data sheets, sql databases, e.t.c. Even pulling valuable insights from unstructured data like images, voices, text e.t.c.
Skills to master for being a data scientist? A data scientist need to know what data to collect and what data to analyse for a given problem statement. He must understand the data collected and he need to draw valuable insight out the data, to do this task data scientist need to be good at Statistics. If the problem given is simple and less amount of data, data scientist can solve the problem using data visualization or data reporting using various tools like tableau or power bi e.t.c. so data scientist need to be good at visualization tools. If the problem is complex and need to work on huge amount of data, we use machine learning to pull various valuable patterns out of data. So a data scientist need to be good at Machine learning skills. Finally and important skills to understand the data or to pull insights from the data related to a problem domain knowledge is must for a data scientist. If you plan for a career as data scientist it's better to learn all this above skills.
Data Science Applications: Data Science implemented across different industries like finance, healthcare, manufacturing, IT, Tech, Banking e.t.c. We can find various example like data science like predicting future products, approving a loan, identifying the risk, image recognition, predicting the fall of the person. Heart attack prediction, text predictions, google search, applications towards dental, drug suggestions e.t.c. are some of the examples of data science.
How to start? People who are planning to switch their career towards data science can learn via various podcasts or via various youtube channels or data science wiki or data science pdf. But to switch faster and to have a better real time learning experience it's better and suggested to go under a mentor who is with real time experience so he can fine tune your learning and he can make you to focus on what is important? what to prepare? how to crack a job? How to build resume? e.t.c.

Our data science with python and R Programming is crafted in such a way that even a layman can crack job on data science.

Our data science course contains Statistics, linear algebra, Probability, Python Programming, Machine learning, Domain knowledge on healthcare, IT, Banking, Oil & Gas e.t.c. and Data Visualization with tableau and Power BI.

Each and every concept is carried with real time example as well as with an assignment to work

A data scientist is very known for building prediction models using machine learning techniques like forecasting, regression and classification.

Here in our training you are going to learn nearly 10+ machine learning algorithms like decision trees, ensembling methods, nonparametric methods e.t.c.

Each and every algorithm was ended up with one real  time project from different domain and it's applications and creating documentation for each project on data science.

Nearly 15+ live real  time projects on data science you people going to work in our trainings.

After the training we prepare your resume on data science and market it across different industries and test you with various mock interviews and projects until you crack job on Data Science.
Agenda
BASIC & ADVANCED STATISTICS

PURPOSE OF STATISTICS

DESCRIPTIVE STATISTICS

INFERENTIAL STATISTICS

WHAT IS DATA?

DIFFERENT TYPES OF DATA

PROBABILITY DISTRIBUTION

NORMAL DISTRIBUTION

SKEWNESS & KURTOSIS

HYPOTHESIS TESTING

NULL HYPOTHESIS TESTING, ALTERNATE HYPOTHESIS

STATISTICAL TESTING

1. ANOVA TEST

2. 2SAMPLET TEST

3. 2  PROPORTION TEST

4. CHISQUARE TEST
PYTHON AND R PROGRAMMING

INTRODUCTION TO PYTHON

1. PURPOSE OF PYTHON

2. VALUES, VARIABLES, FUNCTIONS & LIBRARIES

LIST, TUPLE, SET AND DICTIONARY

USER DEFINED FUNCTIONS

FOR LOOP, WHILE LOOP, CASE

DATA CLEANING WITH PYTHON USING PANDAS, SCIKIT LEARN, NUMPY

INTRODUCTION TO R

WORKING ON DATA VISUALIZATION WITH "R"

DATA CLEANING WITH R

PERFORMING STATISTICAL TESTING ON 'R' & PYTHON

DATA HANDLING ON HUGE SERVERS LIKE HADOOP, SQL E.T.C.

PROJECTS ON IMPORTING DATA FROM LARGE SERVERS & SHAPING THE

DATA FOR ANALYSIS
MACHINE LEARNING ALGORITHMS

WHAT IS SUPERVISED LEARNING ?

WHAT IS UNSUPERVISED LEARNING ?

WHAT IS SEMI  SUPERVISED LEARNING ?

WHAT IS REINFORCEMENT LEARNING

DIFFERENCE BETWEEN MACHINE LEARNING & DEEP LEARNING.

DIFFERENCE BETWEEN A.I. & C.I

REGRESSION vs CLASSIFICATION

INTRODUCTION TO LINEAR REGRESSION 
1. SIMPLE LINEAR REGRESSION 
2. MULTIPLE LINEAR REGRESSION 
3. MATHS BEHIND L.R 
4. OLS 
5. BASED ON SLOPE AND INTERCEPT EQUATIONS 
6. GRADIENT DESCENTS

BUILD LINEAR REGRESSION MANUALLY

CODING LINEAR REGRESSION ON PYTHON

VALIDATION TECHNIQUES LIKE

R  SQUARED , MSE, RMSE, AIC, BIC E.T.C.

ASSUMPTIONS OF L.R

REAL TIME BUSINESS CASE EXPLANATION ON L.R

REAL TIME PROJECT  HEALTH CARE DOMAIN ON L.R

MATHS BEHIND LOGISTIC REGRESSION

BUILDING LOGISTIC REGRESSION MANUALLY

CODING LOGISTIC REGRESSION ON PYTHON

VALIDATION TECHNIQUES LIKE CONFUSION MATRIX

ROC, AIC E.T.C.

ASSUMPTIONS ON LOGISTIC REGRESSION

BUSINESS CASE EXPLANATION ON LOGISTIC

REGRESSION

BUSINESS CASE ON LOGISTIC REGRESSION FROM BANKING

K  MEANS CLUSTERING

H. CLUSTERING

MARKET SEGMENTATION USING K  MEANS CLUSTERING

CLUSTERING FOR DATA IMPUTATION

CLUSTERING IN CYBER SECURITY

H . CLUSTERING TO UNDERSTAND THE PURCHASE PATTERN

WORKING ON A REAL TIME PROJECT BASED ON ABOVE 3

ALGORITHMS

DECISION TREE  INTERNAL MECHANISM

ENTROPY & GINI

HYPER PARAMETER TUNING

CONTROLLING THE OVER FITTING

BAGGING & BOOSTING FOR DECISION TREE AND MANUALLY BUILDING A

BASIC DECISION TREE FOR A SAMPLE DATA

HOW TO BUILD A POC ?

EXAMPLE POC BUILDING.
PICK ONE ELECTIVE TOPIC

CONVOLUTION NEURAL NETWORK(IMAGE RECOGNITION)

DEPLOYMENT OF MACHINE LEARNING MODELS

BUILDING REINFORCEMENT LEARNING MODELS

HIDDEN MARKOV CHAINS
SCHEDULE:
Reviews
Amarendra Pendyala
Best for all those analytical courses at various locations....and different mode of trainings....Best Planned Strategies
Yakub Pasha
I took 2 courses in BEPEC, the content is very impressive, i completed the courses recently,i suggest bepec for both freshers & experienced, Thank you BEPEC
Medhasri Manda
Great institute to start up your career in analytics field. The materials are very exciting. The best part is the virtual lab where you get hands on experience. trully brilliant. There is so much to learn.
Excellence teaching... Super guidence to get job.. Full tech support..
Certification
How do i earn my Data Science  Best Practices Certification?
PreRequisites for Certified Data Science Consultant  Best Practices
1. Must complete one project on Data Science with Project documentation and Source Code
2. Need to attend 16 Hours of Best Practices on Data Science Training from BEPEC Global
3. Need to book exam with BEPEC Global
4. Must gain 60% to get awarded with CMLD  Best Practices Certification
5. Answer sheet going to be corrected by Artificial Neural Networks
6.Aggregated by Independent Global Certification (IGC)
FAQ'S
BASIC & ADVANCED STATISTICS

PURPOSE OF STATISTICS

DESCRIPTIVE STATISTICS

INFERENTIAL STATISTICS

WHAT IS DATA?

DIFFERENT TYPES OF DATA

PROBABILITY DISTRIBUTION

NORMAL DISTRIBUTION

SKEWNESS & KURTOSIS

HYPOTHESIS TESTING

NULL HYPOTHESIS TESTING, ALTERNATE HYPOTHESIS

STATISTICAL TESTING

1. ANOVA TEST

2. 2SAMPLET TEST

3. 2  PROPORTION TEST

4. CHISQUARE TEST
PYTHON AND R PROGRAMMING

INTRODUCTION TO PYTHON

1. PURPOSE OF PYTHON

2. VALUES, VARIABLES, FUNCTIONS & LIBRARIES

LIST, TUPLE, SET AND DICTIONARY

USER DEFINED FUNCTIONS

FOR LOOP, WHILE LOOP, CASE

DATA CLEANING WITH PYTHON USING PANDAS, SCIKIT LEARN, NUMPY

INTRODUCTION TO R

WORKING ON DATA VISUALIZATION WITH "R"

DATA CLEANING WITH R

PERFORMING STATISTICAL TESTING ON 'R' & PYTHON

DATA HANDLING ON HUGE SERVERS LIKE HADOOP, SQL E.T.C.

PROJECTS ON IMPORTING DATA FROM LARGE SERVERS & SHAPING THE

DATA FOR ANALYSIS
MACHINE LEARNING ALGORITHMS

WHAT IS SUPERVISED LEARNING ?

WHAT IS UNSUPERVISED LEARNING ?

WHAT IS SEMI  SUPERVISED LEARNING ?

WHAT IS REINFORCEMENT LEARNING

DIFFERENCE BETWEEN MACHINE LEARNING & DEEP LEARNING.

DIFFERENCE BETWEEN A.I. & C.I

REGRESSION vs CLASSIFICATION

INTRODUCTION TO LINEAR REGRESSION 
1. SIMPLE LINEAR REGRESSION 
2. MULTIPLE LINEAR REGRESSION 
3. MATHS BEHIND L.R 
4. OLS 
5. BASED ON SLOPE AND INTERCEPT EQUATIONS 
6. GRADIENT DESCENTS

BUILD LINEAR REGRESSION MANUALLY

CODING LINEAR REGRESSION ON PYTHON

VALIDATION TECHNIQUES LIKE

R  SQUARED , MSE, RMSE, AIC, BIC E.T.C.

ASSUMPTIONS OF L.R

REAL TIME BUSINESS CASE EXPLANATION ON L.R

REAL TIME PROJECT  HEALTH CARE DOMAIN ON L.R

MATHS BEHIND LOGISTIC REGRESSION

BUILDING LOGISTIC REGRESSION MANUALLY

CODING LOGISTIC REGRESSION ON PYTHON

VALIDATION TECHNIQUES LIKE CONFUSION MATRIX

ROC, AIC E.T.C.

ASSUMPTIONS ON LOGISTIC REGRESSION

BUSINESS CASE EXPLANATION ON LOGISTIC

REGRESSION

BUSINESS CASE ON LOGISTIC REGRESSION FROM BANKING

K  MEANS CLUSTERING

H. CLUSTERING

MARKET SEGMENTATION USING K  MEANS CLUSTERING

CLUSTERING FOR DATA IMPUTATION

CLUSTERING IN CYBER SECURITY

H . CLUSTERING TO UNDERSTAND THE PURCHASE PATTERN

WORKING ON A REAL TIME PROJECT BASED ON ABOVE 3

ALGORITHMS

DECISION TREE  INTERNAL MECHANISM

ENTROPY & GINI

HYPER PARAMETER TUNING

CONTROLLING THE OVER FITTING

BAGGING & BOOSTING FOR DECISION TREE AND MANUALLY BUILDING A

BASIC DECISION TREE FOR A SAMPLE DATA

HOW TO BUILD A POC ?

EXAMPLE POC BUILDING.
PICK ONE ELECTIVE TOPIC

CONVOLUTION NEURAL NETWORK(IMAGE RECOGNITION)

DEPLOYMENT OF MACHINE LEARNING MODELS

BUILDING REINFORCEMENT LEARNING MODELS

HIDDEN MARKOV CHAINS
Podcasts
Data Science Definition:
Data Scientist is the person who extract information or valuable insight out of structured data like rows and columns eg: your excel data sheets, sql databases, e.t.c. Even pulling valuable insights from unstructured data like images, voices, text e.t.c,A data scientist need to know what data to collect and what data to analyse for a given problem statement. He must understand the data collected and he need to draw valuable insight out the data, to do this task data scientist need to be good at Statistics. If the problem given is simple and less amount of data, data scientist can solve the problem using data visualization or data reporting using various tools like tableau or power bi e.t.c. so data scientist need to be good at visualization tools. If the problem is complex and need to work on huge amount of data, we use machine learning to pull various valuable patterns out of data. So a data scientist need to be good at Machine learning skills. Finally and important skills to understand the data or to pull insights from the data related to a problem domain knowledge is must for a data scientist. If you plan for a career as data scientist it's better to learn all this above skills.
Our Hiring Partners
Our Services
Knowledge on
Q & A's
Project Review
Personal Mentor
Weekly Sessions
Personalised Learning Plan
Demo On Data Science
1.Testimonial on Data Science job
2.Mistakes in Data Science Resumes
3.Learning Data Science IN Wrong way
4.Difference between Traditional Statistics and Modern Statistics
5.Certifications or Project?
READ INTERESTING ARTICLES, TAKE UP EXAMINATIONS, SEE UPDATED PROJECTS E.T.C.
CHECK OUR VIDEOS, INTERVIEWS, BEHIND THE CAMERA, WEBCASTS, PANEL DISCUSSIONS E.T.C.
ANY QUERIES CHAT WITH US ON TWITTER. POST YOUR Q & A's
CHECK ABOUT OUR RECENT ACTIVITIES, EMPLOYEES FUN, STUDENT INTERVIEWS, STAY UPDATED WITH OUR ACTIVITIES
Avinash
Daily assignments helped to grab more topics on tensorflow
Satya
Great guidance and support from the trainer
Ranvish
Best job support from BEPEC
Sundar
Excellent faculty, i got trained from two trainers with real time examples and virtual lab helped me out!
Pranav
Training was topnotch, highly dedicated trainers until you get placed.