honest-dataisgood-reviews

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Dataisgood: Data science training and job placement services

DataisGood is a leading provider of data science training and job placement services. The course is designed to provide learners with the knowledge and skills they need to launch a successful career in data science. Here's a detailed review of what the course offers.

Expert faculty and friendly instructors:

DataisGood has an expert faculty comprising industry professionals with years of experience in data science. The instructors are friendly and make tough subjects fun and playful. Learners get a team of mentors and subject matter experts for doubt clearing sessions.

The counselors are knowledgeable, with names like Nancy Bajaj, Shailesh, Swati, Shraddha, among others. The mentors include Leeladhar Sir and Arpit Sir, while the instructors are Arun Sir, Sumit Sir, Joe Sir, Arpit Sir, and Sanket Sir. The student projects placement team is led by Subhangi Mam and Vidisha Mam.

Read this Github articles also of Data Science program for Dataisgood

Tailored Curriculum:

DataisGood provides a platform that will leave no stone unturned in their bid to make you a Rockstar Data Scientist. They have meticulously planned the journey for a promising and fulfilling data science career ahead. The curriculum is designed to help learners optimize investments made by a client using statistics and machine learning.

Tackling a client project involves helping the customer understand portfolio management and estimation of risks using different statistical and financial models. Finally, learners help the client to optimize their investments by suggesting the most appropriate portfolio.

Learners also learn how to estimate the lifetime value for the customers of an e-commerce giant. They build a machine learning predictive model to explain and articulate the lifetime validation and valuation for the customers who have ever purchased or visited the e-commerce giant using advanced statistical techniques. Finally, they help the e-commerce giant to understand the value of each customer.

Another essential aspect of the curriculum is helping medical experts choose the best drug for each condition using NLP. With the coming age, thousands of drugs are coming for each medical condition, making it very difficult for medical professionals to keep themselves updated about the best drugs. Using advanced text processing and NLP techniques, learners help identify the best drugs for each medical condition using patient reviews.

Capstone Project:

The capstone project is designed to give learners a taste of what data scientists go through in real life when working with data. It allows students to create a usable/public data product that can be used to show their skills to potential employers. The project also involves building an amazing and powerful capstone project in the domain that suits them the most. Learners learn advanced techniques and tools used for building real-world engineering solutions with a team of highly motivated students.

Collaboration and support:

DataisGood provides learners with a team of teaching assistants who are always ready for supporting, guiding, and helping them out in implementing their ideas from scratch. In addition to TAs, learners also have access to personalized mentors for any type of guidance. They also get to consult with domain experts to understand the usage of ML models in the real world. Learners brainstorm amazing ideas for building and designing their project and get reviews and feedback from experts.

Certification and career support:

DataisGood's course comes with professional certification that proves learners have acquired the necessary skills and knowledge to launch a career in data science. The course also includes dedicated career coaches who help learners with job placement services. They provide learners with job readiness training, interview preparation, and networking opportunities to help them land their dream job.

Conclusion:

DataisGood's course is designed to help learners launch a successful career in data science. With expert faculty, friendly instructors, and a tailored curriculum, learners acquire the necessary skills and knowledge to become rockstar data scientists.

Overall, DataisGood's comprehensive course and supportive community make it a top choice for anyone looking to enter or advance in the exciting field of data science.

Here is full details of Data science course at Dataisgood

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Mastering SQL Queries: Tips and tricks for data analysis

Structured Query Language (SQL) is a powerful tool for data analysis that enables users to extract, manipulate, and analyze data from databases. SQL queries can be used to perform a wide range of tasks, from simple data retrieval to complex data analyses. In this article, we will explore some tips and tricks for mastering SQL queries and conducting effective data analysis.

Start with the basics: SELECT, FROM, and WHERE

The SELECT statement is the most basic and important SQL command, used to retrieve data from a database table. The basic syntax for the SELECT statement is:

SELECT column1, column2, ... FROM table_name WHERE condition;

The FROM clause specifies the table or tables from which the data is to be retrieved, while the WHERE clause is used to filter the data based on specific conditions. By mastering the basic SELECT, FROM, and WHERE statements, data analysts can retrieve data that is relevant to their analysis and filter out unnecessary data.

Read also this medium articles Why SQL is essential.

2.Use JOINs to combine data from multiple tables-

In many cases, data analysts may need to combine data from multiple tables to conduct their analysis. This is where JOIN statements come in handy. JOIN statements allow analysts to link data from multiple tables based on common columns, thereby enabling them to analyze data that is distributed across multiple tables.

For example, suppose a data analyst wants to analyze the relationship between customer demographics and purchase behavior. The analyst may need to join data from the customer and purchase tables using the customer_id column, which is a common column between the two tables. The SQL query to join the customer and purchase tables could be:

SELECT * FROM customers JOIN purchases ON customers.customer_id = purchases.customer_id;

This query will join the customers and purchases tables on the customer_id column and retrieve all columns from both tables. By using JOIN statements, analysts can combine data from multiple tables and conduct more comprehensive analyses.

3. Group data using the GROUP BY statement-

The GROUP BY statement is a powerful tool for data analysis that enables analysts to group data based on one or more columns. The GROUP BY statement is typically used in conjunction with aggregate functions, such as SUM, AVG, MIN, and MAX, to perform calculations on grouped data.

For example, suppose a data analyst wants to calculate the total purchase amount by product category. The SQL query to calculate this could be:

SELECT product_category, SUM(purchase_amount) AS total_sales FROM purchases GROUP BY product_category;

This query will group the purchase data by product category and calculate the total sales for each category. By using the GROUP BY statement, analysts can gain insights into the distribution of data across different categories and identify trends or patterns in the data.

4. Use Subqueries to simplify complex queries-

Subqueries are SQL queries that are nested within other SQL queries. Subqueries are used to simplify complex queries and break them down into smaller, more manageable components. Subqueries can be used in a variety of ways, such as to filter data, calculate aggregate values, or create temporary tables.

For example, suppose a data analyst wants to calculate the average purchase amount for customers who have made at least one purchase in the past 30 days. The SQL query to calculate this could be:

SELECT AVG(purchase_amount) AS average_purchase_amount FROM purchases WHERE customer_id IN ( SELECT DISTINCT customer_id FROM purchases WHERE purchase_date >= DATEADD('day', -30, CURRENT_DATE) );

This query uses a subquery to retrieve the customer_ids of customers who have made a purchase in the past 30 days, and then calculates the average purchase amount for those customers. By using subqueries, analysts can simplify complex queries and break them down into smaller, more manageable components.

5. Use CASE statements to categorize-

CASE statements are used to categorize data based on specific conditions or criteria. CASE statements allow analysts to create new columns that categorize or group data based on specific criteria, enabling them to analyze data in a more meaningful way.

For example, suppose a data analyst wants to categorize customers based on their purchase behavior. The analyst may want to create a new column that categorizes customers as high, medium, or low spenders based on their total purchase amount. The SQL query to create this new column could be:

SELECT customer_id, total_purchase_amount, CASE WHEN total_purchase_amount > 1000 THEN 'High Spender' WHEN total_purchase_amount > 500 THEN 'Medium Spender' ELSE 'Low Spender' END AS spending_category FROM purchase_totals_by_customer;

This query uses a CASE statement to create a new column called spending_category that categorizes customers based on their total purchase amount. By using CASE statements, analysts can categorize data based on specific conditions or criteria, enabling them to gain deeper insights into the data.

Conclusion:

SQL is a powerful tool for data analysis that enables analysts to extract, manipulate, and analyze data from databases. By mastering SQL queries, analysts can retrieve data that is relevant to their analysis, combine data from multiple tables, group data based on specific criteria, simplify complex queries, and categorize data based on specific conditions or criteria. By using these tips and tricks, analysts can conduct effective data analysis and gain deeper insights into their data.

Here is also good articles in Dataisgood.

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10 Ways Dataisgood's Data Science Course Transformed My Career

Introduction:

Breaking into the world of data science can be challenging, but finding the right program can make all the difference. After discovering Dataisgood's data science course, I embarked on a journey that transformed my career. In this article, I will share my personal experience and highlight the ten major advantages I gained from the program.

A Strong Foundation: The program began by introducing me to the essential aspects of data science, including programming languages and tools. This laid a sturdy foundation for success and boosted my confidence.

Data Visualization: The program delved deep into the fascinating world of data visualization, allowing me to identify trends and patterns in data and develop the art of storytelling with data. This skill has been invaluable in my professional life.

Data Wrangling: Data cleaning and manipulation were critical skills I acquired during the course. Honeing these abilities significantly improved my efficiency when processing data for analysis, allowing me to tackle complex projects with ease.

Statistics: The program's focus on statistics and hypothesis testing allowed me to develop the confidence to make data-driven decisions as a data scientist.

Advanced Machine Learning: I learned state-of-the-art machine learning algorithms and techniques, empowering me to leverage machine learning to solve intricate problems and stay competitive.

Natural Language Processing: Acquiring expertise in NLP techniques and their applications has opened up new opportunities for me in industries that rely on text-based data.

Artificial Intelligence: The program's foray into deep learning and neural networks ignited my interest in the future of AI. Now, with the ability to develop advanced AI models, I'm eager to explore diverse applications and make a lasting contribution to the industry.

API Building and Deployment: Learning the process of deploying machine learning models and managing APIs has proven to be extremely valuable, allowing me to transform my models into functional applications.

Tableau Data Visualization: Becoming proficient in data visualization with Tableau has revolutionized the way I communicate insights. I can now create engaging data narratives that effectively convey my findings to both technical and non-technical audiences.

Big Data Handling: Finally, the program taught me the essentials of handling big data using Spark. Being able to efficiently process massive datasets, I've been able to take on more ambitious projects and deliver impactful results.

Read this Github article for Data science courses & certifications

Extra Perks: The program offered 200+ hours of self-paced training, live classes, hands-on projects, and collaboration with domain experts. I also benefited from the unwavering support of teaching assistants and professional grooming by industry leaders.

Conclusion:

Dataisgood's data science course transformed my career and equipped me with the skills and knowledge needed to excel in the field. From a strong foundation to advanced AI, big data handling, and Tableau data visualization, this program covers all the bases. The extra perks, including self-paced training, live classes, hands-on projects, and support from industry experts, make this course a must-try for anyone looking to break into data science or take their skills to the next level.

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Dataisgood: Revolutionizing Tech Education with Guaranteed Job Program in Data Science

The surge in demand for data science professionals is a call to action for the tech industry to produce industry-ready data scientists. Stepping up to this challenge is Dataisgood, a fiercely independent tech education company that is transforming the educational landscape with its exceptional job-guaranteed data science program.

Dataisgood's immersive 12-month course in data science is meticulously designed to equip learners with the skills they need to make an impact in the technology industry. The comprehensive curriculum encompasses modules such as Exploratory Data Analysis, Data Wrangling, Statistics and Hypothesis Testing, Advanced Machine Learning, Natural Language Processing, Introduction to Deep Learning, API Building Model Deployment, Tableau for Storytelling, and Big Data with Spark.

What sets Dataisgood apart is its exceptional job guarantee feature. The company is committed not only to providing high-quality education but also ensuring that their learners secure their dream jobs in top-tier technology companies after completing the course. This unique selling proposition has made Dataisgood the preferred choice for aspiring data scientists.

A quick look at the Dataisgood reviews reveals the success of this initiative. Students widely appreciate the quality of instruction, the course structure, and most importantly, the excellent placement record. Countless learners have shared their triumphant transition into data science roles following the completion of the course, attesting to the program's effectiveness.

Aligned with their mission to make world-class tech education accessible to everyone, Dataisgood aims to inspire, motivate, challenge, and empower individuals. The company's philosophy is not just to facilitate learning but to instill a passion for the subject, creating lifelong learners and explorers.

To learn more about the experiences of learners and their successful career transitions, visit the Dataisgood reviews page. The stories of individuals who have embarked on their data science journey with Dataisgood are sure to inspire you to take the plunge into this exciting field.

Dataisgood is not just an educational platform; it's a launchpad for tech aspirants to reach new heights in their careers. Their job-guaranteed data science program is a testament to their commitment to bridging the skill gap in the tech industry and nurturing the next generation of data scientists.

Join Dataisgood today and take your first step towards a promising career in data science. The future of tech education is here, and with Dataisgood, you're on the right path.

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