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“Ironically, IT produces great team players, but they seem to have a difficult time transitioning from the role of individual contributor to that of manager. They follow orders well, but have not learned how to delegate to others.

Few professionals today expect to work for the same employer their entire career. Change is the daily reality for all industries and all businesses, and most certainly for the IT world. Increasing global competition, evolving technology, and shortened product life cycles have forced companies to become more nimble and flexible. For this reason, there is a growing realization that multiple careers during a lifetime are not only possible, but probable.

Unfortunately, people are often not prepared for change when it does take place. We are not a society that encourages continuous self-examination, and most people never do any career management until they are forced. Whether you encounter a company downsizing or the appearance of an attractive offer, it is almost always an external event that grabs you and gives you the impetus to take responsibility for yourself and your future. Being prepared for change can make all the difference.

Change is in the air

Preparing for change can be difficult for those who are good at, and enjoy, what they do. IT professionals are often in this category. IT pros can become so wrapped up in their technology that other skills are left wanting, either because they have no interest in developing such expertise or because they mistake learning new technologies for career growth. Ironically, IT produces great team players, but they seem to have a difficult time transitioning from the role of individual contributor to that of manager. They follow orders well, but have not learned how to delegate to others.

One way to prepare for the inevitable changes you will face in your career is to take courses that build non-technical skills and prepare you for a variety of career opportunities. For example, a shortage of good technology savvy managers is one of the biggest problems facing American business today. Those who take the time to learn these in-demand skills, such as team building, communication, negotiation and basic business and management techniques, will be better prepared to deal with change and increase the number of new job opportunities available to them.

Other ways you can begin to prepare for change include:

Demonstrate initiative. Show you are eager to outgrow your job. Identify problem areas and act to correct them.

Seek opportunities for self-improvement. Request special training, or volunteer for new assignments that require using new skills. Switch tasks with a peer. Volunteer for committees that are multi-functional or multi-business in nature.

Exercise leadership. Help co-workers and provide direction when appropriate. Take charge of special projects, particularly those requiring inter-departmental involvement.

Become active in professional organizations. Participate on committees, or seek a position on the board of directors.

Moving on

Increasingly, those making a career change are not merely upgrading to another position in the company, but are heading out the door into an entirely new situation. A growing trend for IT professionals is to move from a large company to a small start-up, either to work for someone else or to launch their own enterprise. People frequently do not understand the consequences and potential pitfalls of such a major change of venue, and that can be a huge mistake.

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How To Change Career From Data Analyst To Data Scientist?

People constantly deal with data, and Data Analysts look for more challenging roles after gaining expertise in their domain. Data Scientist is often considered one of the most lucrative career options. Though it requires expansion of skill set, several educational platforms offer insightful knowledge that favors change. Numerous data analysts have successfully taken the switch, and you can be the next!

The following steps will help you contribute to the company’s development and add to your expertise as you embark on your career as a data scientist:

Assessing the Skills Gap Essential Skills And Knowledge Required For A Data Scientist Role

Data scientists need to experiment with data, so the mindset of developing new ideas and research is crucial. Equally important is the ability to analyze the mistakes from past experiments. Adding to these, the technical skills and knowledge required to carry out the duties things are as follows:

Technical skills: Analytical Skills:

API tools like IBM Watson, OAuth, Microsoft Azure

Experimentation and A/B testing

Predictive modeling and statistical concepts such as regression, classification, and time series analysis

Domain Knowledge:

Postgraduate qualifications such as a Master’s or Ph.D. in computer science, software engineering, or statistics

Subject Matter Expertise

Soft Skills:



Curiosity and Continuous learning


Overlapping Skills Between Data Analysts And Data Scientists

Both data analysts and data scientists have to:

Data Manipulation, Processing, and Preparation: Data analysts perform the actions for transforming raw data into a usable format, while scientists are concerned with model training.

Automation: The analyst automates the data to streamline repetitive tasks such as processing and report generation. Scientists work to automate feature engineering and model deployment.

Analysis: Analysts explore and uncover insights through research, while scientists use statistical analysis for deeper understanding and interpretation.

Visualization: The analysts make interesting visualizations of complex data for stakeholders while scientists communicate feature distribution, model performance, and outputs to stakeholders and collaborators.

Data Query: Analysts use data queries for specific subset retrieval, filtering, and report generation. The scientists act to extract data for model training and evaluation.

Programming: Analysts are not as profoundly familiar with codes as scientists; the former can write code snippets or scripts while the latter write the complete programs for the implementation and execution of machine learning algorithms.

Statistical analysis:  Data analysts validate hypotheses and understand relations with statistical analysis while scientists evaluate model performance, check significance and reliability, and interpret results.

Areas Where Additional Skills And Knowledge Are Needed For The Transition Practical Skills:

Ability to design experiments and A/B tests and understand its principles and methodologies for conducting valid and reliable experiments.

Working with large datasets

Implementation of data pipelines

Data storage optimization and retrieval

Technical Skills:

Knowledge of neural networks, hyperparameter tuning, and model optimization

Domain Knowledge:

Industry-specific knowledge and understanding of the internal functionality of the industry

Formulate data-driven solutions for core domains of the industry

Knowledge of business principles, market dynamics, and economics

Soft Skills:

Storytelling and communication

The project management ability to handle complex projects and multiple stakeholders

Will constantly learn and adapt to new technologies

Remain competitive and innovative

Source: CompTIA

Bridging the Gap: Skill Development Exploring Educational Resources And Learning Paths For Acquiring The Necessary Skills

Both offline and online platforms provide numerous quality resources, such as books in pdf format, worksheets for practice, and free access to tools and programming languages. The learning journey becomes relatively more straightforward by joining learning paths and taking certified online courses from quality educators imparting practical knowledge.

Importance Of Acquiring Knowledge Of Programming Languages Like Python Or R

Python serves functionality for data manipulation and analysis through libraries like NumPy and SciPy, useful for preprocessing, wrangling, cleaning, and analysis of data along with exploratory data analysis. It is also the go-to language for machine learning tasks through supporting libraries such as PyTorch and TensorFlow suitable for building data models. Also providing options for data visualization, Python is preferred for web scraping and data collection through its unique and extensive library set.

Significance Of Statistics, Machine Learning, And Data Visualizations Skills

The transition from data analyst to data scientist requires understanding of specific skills. Statistics provides a base for hypothesis testing and experimental design through information on designing experiments and formulating hypotheses. It evaluates the idea by finding the significance and validation of assumptions. Statistical modeling techniques such as regression, survival, and time series analysis are essential for building predictive models. These are significant for understanding factors having a role in specific outcomes.

Machine learning helps data scientists formulate algorithms and models for decision-making and predictability without programming. These algorithms are essential for historical data prediction, which analyzes complex patterns and relationships in data. It also allows image recognition, recommendation systems, customer segmentation, fraud detection, and categorization of new data as per the defined criteria.

Data visualization skills help convey the information in interactive and storytelling format, which is helpful in decision-making and driving action based on depicted data. The data visualization skills include the identification of outliers, trends, and distribution, thus guiding data scientists to deep insights, hypothesis generation, and detection of patterns and anomalies.

Role of online courses, boot camps, and self-study in skill development

Online courses



provide recorded or sometimes live lectures, quizzes, assignments, and projects. The comprehensive collection of classes helps develop skills based on the learner’s pace. Expert guidance and hands-on practical experience are suitable for upskilling and becoming familiar with real-world trends. 

Boot camps: 

They are intensive and immersive programs that encourage students’ transition to data science roles in a systemized manner, making them ready for the job. Inculcating job-based skills is similar to company training, which must include live interaction sessions with leaders in a specific field. Direct interaction, mentorship, and career support are often seen in boot camps contributing to better skills and opportunities for networking. 


Self-study is a practical approach requiring self-determination. It involves the self-organization of numerous available notes. However, it comes with a customized learning approach where candidates can formulate their schedules and work based on their strengths and weaknesses. 

Source: ResearchGate

Gaining Practical Experience Importance Of Hands-On Experience In Data Science Projects

Hands-on experience is crucial to achieving functionality, updates on current trends, and the ability to work with others in a specific field. The experience familiarizes the candidates with real-world problems, helps them understand data complexity, and allows time and opportunity to explore various techniques.

Ways to Gain Practical Experience

Internships: Regardless of the stipend, internships are a great source as they familiarize the candidates with the field and work. It helps in gaining insights while learning. Analytics Vidhya is hiring data science interns to help them accomplish their dreams.

Freelancing: An experienced and independent individual can go for freelancing, where they learn communication skills, use their expertise in analytics, earn money, and exhibit their work.

Analytics Vidhya

provides a guide to step forward in this direction. 

Data Science Competitions: This help brings forward candidates’ competitive edge and exposes their ability to work under pressure. Also, working on the innovative bend of mind, candidates must participate in

data science competitions


Hackathons: Hosting numerous Data Science Competitions in Analytics Vidhya’s

Data Hack

, buckle up to prove your worth. There will be numerous competitions to participate in, along with networking opportunities with leaders of Data Science.

Joining internships, regardless of stipends, is the most appropriate approach to gaining experience. It requires cracking interviews and proving yourself to enter the field. Academic research projects, freelancing, or consulting work must also be looked forward to becoming familiar with real-world trends and requirements in data science. Collaboration, data science competitions, and hackathons provide the right platform for practical experience.

Significance Of Collaborative Projects, Internships, And Industry Certifications

Collaborative projects in data science fill individuals with diverse perspectives and the art of working in a team. It expands the knowledge base and ability to collaborate with other field experts. It exposes the candidates to alternative approaches and creative solutions and adds to the skills of different fields or industries relevant to the job role. The networking opportunities are the most significant benefit.

Due to certificate awards and performance reviews, internships are complete proof of working in the corporate world or field. It helps in professional development through interaction with experts and supervisors enlightening the candidates about possible career paths and opportunities.

Industry certificates are the best way to validate skills and knowledge base. It helps in closing the skill gaps and gaining recognition by employers. It also increases networking and knowledge through ongoing industry learning and renewal programs.

Leveraging Transferable Skills Identify The Transferable Skills From A Data Analyst Role To A Data Scientist Role

There are overlapping skills expected in the data scientist role that can be transferred when transitioning to a data scientist position. They are data manipulation, preprocessing, transformation, and cleaning. The ability to analyze, visualize and interpret data can be transitioned too.

Relevance of Skills like Data Cleaning, Data Exploration, and Problem-Solving

Data cleaning: 

It is required to adhere to the high data quality achieved by removing incomplete mess and errors. It serves as a foundation for analysis and modeling. Data cleaning is also crucial for gaining deep insights into the data and is responsible for the trustworthiness and representation of the information. It helps in minimizing the risk of flawed decisions and incorrect conclusions. 

Data exploration: 

It is required for clear data understanding, pattern identification, insights, and derivation of relationships. It familiarizes them with the structure and variables of datasets. Data exploration also aids in the title of features that impact the target variable and analyzes data relationships with variables. It also contributes to data visualization by uncovering anomalies, outliers, and trends in data. 


Data scientists deal with repeated multiple experiments where the most important thing is to analyze the problem leading to discrepancies in results. The essential skills guiding them to solutions are analytical approach and problem-solving skills. It is also helpful in dealing with industry-based challenges. 

Importance Of Effective Communication And Storytelling In Data Science

These non-technical skills of data scientists are essential to connect with stakeholders. Data scientists also handle teams of juniors where the insights or interpretations coupled with decisions must be communicated. The clarity in how, why, when, and where helps understand and builds trust in the process and leader.

Building a Professional Network Benefits Of Networking In The Data Science Community

Data scientists need to focus on networking as it benefits through:

Continuous Learning: The different people in the industry hold distinct expertise while working on their projects. Communication with them enlightens one about the current trends and technologies.

Innovation: The working knowledge of interdisciplinary fields contributes innovative ideas to cutting-edge research. People from different fields can work together to solve existing loopholes and increase their areas of expertise.

Resources: Gaining familiarity with different domains increases opportunities. One can also utilize other software and databases creatively for their functionality while gaining resources through communication. It effortlessly benefits the workability of individuals while saving time.

Guidance: Connecting with experts, mentors, and professionals is one of the best methods for direction in career choices, work, and technical challenges. It also exposes individuals to various experiences, challenges, and opportunities, paving paths for professional development.

Widen Perspective: Learning the works, methodologies, and methods to tackle different projects widen the perspective arising innovation.

Explore Networking Opportunities Through Industry Events, Conferences, And Online Communities

Owing to the numerous benefits of networking in data science, multiple methods exist to increase connections. The industry events and conferences invite numerous field-based personalities and experts, including professionals, researchers, industrialists, practitioners, and educators.

The tech conferences, meetups, and user groups focusing discussions on data science, AI summits, and world conferences are good sources, regardless of the online or offline mode.

Online communities allow global connectivity from the comfort of home. Bridging the gap between time zones, these are a good source of collaboration with expert individuals in the field.

Further, online communities also include hackathons, open-source projects, online courses, and webinars that help actively engage the community and share knowledge and skills.

Showcasing Your Skills Importance Of Creating A Solid Data Science Portfolio

A solid data science portfolio is an excellent way to showcase the technical skills and expertise gained through different opportunities such as internships, employment, research, projects, or other methods. Exhibiting the courses, educational qualifications, practical application of knowledge, and references serve as an identity and spokesperson of an individual. Providing the mode to stand out from the crowd, the data science portfolio serves as an exhibitor of the success or failure of tasks, providing the candidate with an opportunity to explain their valuable learnings from them.

Explore Ways To Showcase Your Skills Through Projects, GitHub Repositories, And Online Platforms

These three serve as great sources to showcase skills and share the works. To share the skills through data science projects, select the relevant tasks that fit your career goals and highlight the gained expertise. Ensure a clear definition of the problem statement for clarity and a logic-based choice of the approach used to overcome the challenges. It includes methodologies, algorithms, techniques, and using different tools. The project documentation must be clarified by incorporating flowcharts, graphs, and pictures per the requirement. Have proper indexing for more straightforward navigation and precisely communicate what is required and intended directly. Project the impact and results with efficiency while avoiding fake and error-based consequences.

Create the GitHub repository to display the data science projects exhibited in an organized manner. Add the readme file in each warehouse and summarize the projects comprising objectives, methodologies, key findings, visualization, results, and any other relevant detail, if present. Use the version control feature to find the changes and collaboration with other individuals in the field. Ensure adding credits to the collaborators. You can also add the links to projects created on Jupyter Notebooks in the Readme file on GitHub for better interaction and visibility of work.

You can also showcase your works on online platforms such as blogs, portfolios, communities, and Kaggle. Platforms like Medium allows data science blogs or finding other relevant online portfolios for expressing your contribution to the field. Leverage the power of data science communities like Reddit, DataCamp community, or Data Science Central for sharing, discussion, and feedback from others. Use LinkedIn to showcase your works or participate in Kaggle competitions for engagements and seminars.

Source: GitHub Docs

Highlight The Significance Of Demonstrating The Impact Of Your Work Through Case Studies And Data Storytelling

The demonstration through case studies and storytelling helps to communicate the value and relevance of data science to a broad audience, irrespective of technical knowledge. It helps increase familiarity with the topic, understand the impact of problems on different audiences, and develop innovative solutions benefiting humanity. It helps professionally by adding value and impact to portfolio and profile while applying gained skills in data science.

Data storytelling enhances communication skills by simplifying complex problems and making connectivity interactive. It contributes to higher engagement, further easing and introducing problem-solving, the immensely valued approach. It aids in connectivity and relatability with the listeners, leading to successful sessions.

Navigating the Job Market Insights Into The Data Science Job Market And Its Requirements

Data science jobs are rapidly increasing, and its market size is expected to grow at a CAGR of 26.9% from 2023 to 2027. In 2023, the market size is estimated to be about 70.376 USD Bn. Besides increasing demand, you must also consider the growing application of the field in different industries, which helps to find a job as per the candidate’s interest and specialization. The list includes technology, e-commerce, finance, healthcare, and marketing.

Source: Grand View Research

Strategies For Tailoring Your Resume And Cover Letter For Data Science Roles

Your resume and cover letter speak on your behalf and are the primary deciding factor in judging your suitability for the role.

Research the Job Requirements: The foremost thing is to thoroughly understand the job description to identify skills, responsibilities, and qualifications necessary for the role, type of programming languages, tools, industry knowledge, and algorithm. Find if you are the right fit for the position and possess the exact qualifications significant there.

Highlight Relevant Technical Skills: Showcase your relevant technical skills in your resume. Adding education, extra certifications in courses or programming languages, and job role keywords will help select company resumes.

Showcase Data Science Projects: Add a distinct project section mentioning detailed information on the works or your contribution. Ensure to state the quantity or impact of the result on the company in terms of increase in revenue, savings, accuracy improvements, or other such data.

Demonstrate Analytical and Problem-Solving Skills: Exhibit a section stating skills. Enlist your soft skills, such as analytical and problem-solving skills. Relate the same with examples in a crisp manner.

Tailor your Cover Letter: Focusing on a cover letter, perform detailed company research before beginning the writing. Highlight the technical skills necessary for that role and list your unique qualities or abilities to impress the recruiter.

Quantify Achievements and Impact: Mention the achievements on your resume. State the quantitative effect of your accomplishments and the impact caused due to the same. Use numbers or ratings to display the same and the most direct effect it had on the company.

Proofread and Edit: Ensure to proofread the resume and cover letter. Look for grammatical or spelling errors in the company or person’s name. Validate the described qualities or characteristics that match the job role and company. Edit in case of requirement for any changes.

Explore Job Search Platforms, Professional Networks, And Recruitment Agencies For Data Science Job Opportunities

Finding a job is comparatively more straightforward with numerous online platforms. Quality job search platforms include LinkedIn, Indeed, Glassdoor, and Dice. These platforms provide regular updates on different job roles among multiple companies. The platforms offer job alerts to one’s preference for direct updates.

Professional networks and communities provide mentors and connections, providing the right opportunity and guidance to find a suitable role. The communities are available on professional networks such as LinkedIn groups and Kaggle. Connections and personal networking are also possible at meetups and conferences. Recruitment agencies also help in finding the right job roles. Common examples of such agencies include AlmaBetter, Hirist, Harnham, and Korn Ferry.

Source: FactoHR

Why Are Companies Hiring More Data Scientists?

There are multiple reasons leading to the increased hiring of data scientists. It includes excessive data generation and holding confidential information significant for the company’s growth. Processing and interpreting the same is possible by data scientists only. These guide the company to data-driven decision-making, helping make more informed, evidence-based decisions and improving efficiency.

Moreover, data scientists leverage the data to better understand customers’ behavior by understanding their preferences, behaviors, and experiences. Further, data is helpful for risk management and fraud detection to increase operational efficiency and cost reduction.

Source: Interview Query

Upskilling and Continuous Learning Importance Of Ongoing Learning And Upskilling In The Field Of Data Science

Staying updated through the abovementioned methods helps to inculcate efficient problem-solving skills and develop innovation and creativity. It helps to adapt to the industry’s needs and improve performance through the availability of new functionalities.

Success Stories and Advice

Multiple candidates successfully transitioned their careers to Data Science. Success is not limited to the right and deserving income; instead, it expands into career development, happiness, mental peace, satisfaction with their career choice, and proper use of their abilities.

Learning and acting as a data scientist from a petroleum engineer wasn’t easy. The love of mathematics and the opportunity for candidates to deal with data pushed

Jaiyesh Chahar

to change the direction of his career. Having initial knowledge of Data science from his field of job, he took action to learn coding and statistics. Finding a job as a fresher in a new area posed a challenge; however, industry-specific knowledge came to his rescue, helping him land a job with exciting projects. 

Holding experience in Software Test Engineering and Quality Assurance,

Bindhya Rajendran

has worked on real-time data and maintains industry-specific knowledge in equipment manufacturing. She was introduced to analytics through her compulsory training module, where the promising aspects of the field captivated her interest. Taking steps in the right direction, with accurate situation-specific guidance from the founder of Analytics Vidhya, she aced her career choice and is currently working at BOSCH in a Data Analytics specialist position. 

Begin the transition from Data Analyst to Data Scientist by understanding the data science path. Read blogs, books, and online resources to introduce yourself to the field.

Make a table specifying your skills and knowledge and the required ones for transition. Select the relevant ones to learn and choose the preferred courses from Analytics Vidhya. Find the system suitable for your timeline. It includes learning Python, R, Apache Spark, SQL, and Data Visualization tools: Tableau and others.

Familiarize yourself with Machine Learning algorithms such as regression models, decision trees, support vector machines, and gradient boosting. Gain experience by working on projects under your professors or mentors or through internships. Else, be creative and start a project yourself for learning.

Strictly work to expand your network, make connections, and remain in touch with people in your field. Actively look out for opportunities to participate in concerned events. Build an online portfolio.

When you feel satisfied with your learning and experiences and gain enough substantial certifications to prove your caliber, begin the hunt for jobs. You must focus on justifying yourself in the resume and cover letter.

Courses to Take to Become a Data Scientist

Concerning the importance of constant upskilling or transitioning your career from Data Analyst to Data Science, Analytics Vidhya has covered you in every situation. We offer multiple courses concerning the same:

A Comprehensive Learning Path to Become a Data Scientist

It is a beginner-friendly course with an orderly list of resources and course contents. It comprises assignments for testing and serves the prime purpose of upskilling oneself. 

Data Science Career Conclave – Transition to Data Science

It is the right course if you are confused about transitioning into a data science career. It covers essential topics such as different roles and the most suitable for you, panel discussion, methods to build digital profiles, and how to meet the requirements of hiring managers. 

Data Science Immersive Bootcamp

It is a job-guaranteed training program with a record of 100% placement of batches. It has also led to a 250% salary hike and guides you to interview preparation coupled with flexible learning. 


Data Scientist is an intriguing, rewarding, and fascinating profession with constantly evolving requirements of talented and skilled individuals. The ability to work on complex data problems with an analytical and problem-solving mindset and practical approach helps one reach the top in the long run.

Regular upskilling is a crucial factor that helps in one’s professional development. Analytics Vidhya brings you numerous courses regardless of your experience level. Helping you reach your dreams and achieve your goals, we are the helping hands leading you at the peak of your career.

Frequently Asked Questions Related

How To Change Theme For Alertdialog On Android?

What is Alert Dialog in Android?

Alert dialogs in android are used to display alerts within android applications. We can display alert dialog within our application to display any warning message to the user. Inside the alert dialog we can provide two options to the users whether to select as yes or no in the dialog. In this article we will take a look on How to change the theme for default Alert Dialog in Android.

Implementation Step 1 : Creating a new project in Android Studio

Inside this screen we have to simply specify the project name. Then the package name will be generated automatically.

Note − Make sure to select the Language as Java.

Once our project has been created we will get to see 2 files which are open i.e activity_main.xml and chúng tôi file.

Step 3 : Working with activity_main.xml

android:layout_width=”fill_parent” android:layout_height=”fill_parent” android:orientation=”vertical”

<TextView android:id=”@+id/idTVHeading” android:layout_width=”match_parent” android:layout_height=”wrap_content” android:layout_centerInParent=”true” android:layout_margin=”10dp” android:padding=”4dp” android:text=”Custom Styled Alert Dialog” android:textAlignment=”center” android:textColor=”@color/black” android:textSize=”20sp”

<Button android:id=”@+id/idBtnSwitchOff” android:layout_width=”match_parent” android:layout_height=”wrap_content” android:layout_below=”@id/idTVHeading” android:layout_centerInParent=”true” android:layout_margin=”10dp” android:text=”Display Alert Dialog”

Explanation − In the above code we are creating a Relative layout as a root layout and inside that we are creating a text view which we will be using to display the heading of our application. After creating a text view we will be creating a button to open our alert dialog.

Lastly we are adding a closing tag for our Relative Layout.

Step 4 : Adding a custom style in chúng tôi file Step 5 : Working with chúng tôi file package com.example.androidjavaapp; import; import; import androidx.appcompat.view.ContextThemeWrapper; import android.content.BroadcastReceiver; import android.content.Context; import android.content.ContextWrapper; import android.content.DialogInterface; import android.content.Intent; import android.content.IntentFilter; import android.os.Bundle; import android.os.Environment; import android.util.Log; import android.view.View; import android.widget.Button; import android.widget.TextView; import android.widget.Toast; import; import; import; import; import; import; import; import; import org.json.JSONException; import org.json.JSONObject; import; import; import; import java.util.concurrent.TimeUnit; public class MainActivity extends AppCompatActivity { private Button displayAlertDialog; @Override protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); setContentView(R.layout.activity_main); displayAlertDialog = findViewById(; @Override AlertDialog.Builder builder = new AlertDialog.Builder(new ContextThemeWrapper(MainActivity.this,; builder.setMessage("Welcome to Tutorials Point"); builder.setTitle("Welcome"); builder.setCancelable(false); dialog.cancel(); }); AlertDialog alertDialog = builder.create();; } }); } }

Note − Make sure you are connected to your real device or emulator.

Output Conclusion

In the above tutorial we have taken a look on How to create an Alert Dialog for your android application. Along with that we also get to learn on How to change the theme for Alertdialog inside your android application.

What You Should Know For A Career In Artificial Intelligence

Across the realms of consumers and businesses, artificial intelligence (AI) has been wielding great influence and reshaping society as it were. Hailed as a key component of the 4th Industrial Revolution (along with the Internet of Things (IoT), robotics, quantum computing, and other technologies), the rise of AI could well be considered as a seminal stage in the development of humanity.

What is Artificial Intelligence?

Artificial Intelligence (AI), broadly speaking, is the ability of machines to replicate the abilities of human intelligence in tasks such that efficiency is boosted and errors go down. With concepts of basic engineering, mathematics, computer science, linguistics, and psychology, among others, AI bases itself on the fundamentals of reasoning, learning, and problem-solving. A career in artificial intelligence is certainly an attractive prospect.

Related: – Is Artificial Intelligence Replacing Animators?

Where can AI be applied?

AI can be categorized in multiple ways, of which the most common is as below:

Weak/Narrow AI: trained to do specific tasks or functions

Strong AI: capable of making its own decisions when presented with actionable data

AI has great potential in terms of applicability in a variety of sectors. Some examples are given below:

Autonomous driving: Tesla, Google and others have been working for a while on autonomous driving technology, where AI is a basic building block. Ongoing work could soon see wider instances of fully-autonomous driving technology across the world.

Predictive maintenance: proactively scheduling maintenance and hence minimizing costs

Smart cities: better safety, reduced crime, higher energy efficiency

The future uses of AI are even more exciting. Possible applications are many:

Personalized, dynamic pricing: online and offline stores map prices as per consumer behavior

Faster designing of products: aided by quicker sifting through large amounts of customer data through machine learning and deep learning

Related: – The Illusion of CreArtificial Intelligence

How good is the potential for an AI career?

As a technology, AI is still in its initial stages, and there is a lot more that the technology could do with further development. The market is expanding fast and a lot of opportunities keep coming up for a career in artificial intelligence. A survey by job site Indeed suggested that in the UK alone, the number of jobs available in AI has gone up by 485% during 2014-2023 i.e. demand outstrips supply by a factor of 2:1! Across the world, job postings in this field have more than doubled.

Automation and AI were feared as possible death knells for “traditional” jobs, but what has transpired is that AI has in effect created many more jobs than it has taken away i.e. the net effect has been one of new job opportunities coming up. Salaries in the AI domain are skyrocketing too – well-skilled AI professionals in the US can comfortably earn six figures and possibly even higher packages.

How does one begin a career in AI?

Related: – AI Technology skills highest in Demand in 2023

Some of the key skills for AI specialists include:

Bayesian networking (including neural nets)

Computer science (gain coding experience with popular programming languages)

Cognitive science theory




Algebra, calculus, logic and algorithms, probability, statistics

If you are already a software engineer, you could take a definitive step towards a career in AI with some specialized or focused AI courses, many of which are available freely on the internet as well as from recognized institutions and delivered both online as well as through classroom training. If you do not have the time or other resources for long-duration courses, you could choose an AI certification that you could do part- or full-time.

What are the possible career paths?

The possible career paths differ as per the experience level. These include:

Freshers: these would do well to get skilled in mathematics as well as courses in machine learning, programming skills like C++, general business knowledge, and hands-on training.

Programmers: these could start coding by moving to algorithms.

Data analysts and data scientists: these need to strengthen their programming skills by knowing how to prepare data, be proficient at visualizing and building models, and have good communication skills and business knowledge.

The Moral Dilemma Agencies Face With The Google Partners Change

Before we get started I want to make one thing clear: I would love nothing more than for Google Partners to be great.

To be a thriving community of learning, trust, and credibility.

For it to be a place where I can send employees to confidently learn fundamentals.

To be a location that is helpful to both agencies and their clients.

A program that helps everyone succeed with Google Ads.

It would be more than welcomed, it is a need.

The changes announced to the Google Partners program last week aren’t that.

In fact, just when you thought the program couldn’t get worse, it had a “Hold My Beer” moment.

Instead of positively changing the program to create and cultivate a community empowered to serve their clients, the new rules are soiled with sales initiatives that strong-arm agencies to do Google’s bidding – and to do so to the detriment of their clients.

What Changed with the Partners Program?

The new requirements of the Google Partners program will force agencies to hit specific optimization score quotas in client accounts.

These quotas are reached by implementing recommendations that Google provides.

Recommendations that often directly benefit Google.

Previously, agencies could use their judgment and dismiss recommendations (as they often range from detrimental to catastrophic to clients) to increase a campaign’s optimization score.

This is no longer the case, dismissing recommendations will not count toward the account optimization requirement.

Sample recommendations for some of my clients include the likes of:

Increase budget (for a client with fixed budget).

Raise your Target CPA (more than client desires).

Use call extensions (for off-hour campaigns when nobody is at the office to answer the phone).

And the recommendations get even worse the more you look.

Google Partners as a Sales Tool

With this new change, Google has found an all-new sales channel by manipulating Partner agencies into implementing their recommendations.

If you think about it, it’s genius.

Instead of Google reaching out to clients to implement new initiatives, they can leverage Partner program agencies to do their bidding.

This forces the hand of an agency that wants to be a Google Partner.

If optimization score quotas aren’t hit, then a Google Partner will lose their badge.

For those vain agencies that need to be a Partner, this inherently places a dilemma on what actions to take, what they know is best for their clients or Google’s recommendations.

The Moral Issue

Requiring Partners to hit specific optimization score quotas will allow Google to force agencies to do their bidding.

In my professional opinion, Partner agencies will have a good chance of spending more money less efficiently on behalf of their clients (see Do You Really Want a 100% Google Ads Optimization Score?).

Google won’t be on the hook when a client is looking to re-up their contracts, but agencies will (rightly) still be held liable for the work they do.

By forcing agencies to implement Google’s recommendations, the onus is off of Google when it comes to executing those recommendations.

With this change, it is hard to find an outcome other than Google wins everybody else loses.

Over the years, Google Partners has rewarded more and more sales incentives instead of client success incentives:

— Kirk Williams (@PPCKirk) February 4, 2023

Historically, not hitting upsell incentives meant you would miss out on Google Partners store credit.

Now, there is much more on the line than a scooter for the office.

Failing to implement Google’s recommendations (many times upsells) now means forfeiting your Google Partners status. It truly is a despicable thing to do to agencies.

With these changes, the Google Partners program should be renamed the Google Sales Partners program.

The Reality That’s Coming

The Partners program was set up to help people succeed with Google Ads and through the years it’s been neglected, dismantled and is finally turning into a sales tool for Google.

It’s a shame.

It’s debatable how valuable the Google Partners badge is, but what’s not debatable is what partners will be forced to implement beginning in the summer of 2023.

When the Partners program was restructured in 2013 they had the following three objectives:

Connect more closely with Google.

Demonstrate credibility.

Grow your business.

As it stands, there’s nearly no benefit to being a part of this program other than the swag.

After summer 2023, it will be fair to say that Google Partners program agencies have no shame in placing a Partners badge over client success.

While those with spines will leave, the remaining agencies will be invited to all the Google Ads conferences and will be decked out in a bunch of Google Partners merch all while violently hitting the “Apply All” button … much to the chagrin of their clients.

It’s the client’s money being spent and the changes to this program put compliance over competence for agencies, which is a frightening thought.

I’d have them talk through any of Google’s recommendations that they make directly with the client and get their blessing.

What Can We Do?

The easiest thing is to admit that Google doesn’t care about us.

It’s the cold stark bottom-line focused reality.

Don’t worry about the Google Partners program – just do great work.

In summer, the Google Partners program will be much more of a detriment than a help.

On last week’s Marketing O’Clock podcast, I even called it a scarlet letter.

If you go this route and eventually lose your partner’s badge (while killing it for all your clients!) we put together a free little badge that will fit right where that old Google badge used to go.

Our team at Cypress North built a badge system on an independent domain Client.Partners that is free for anyone willing to state:

“We will always prioritize our client’s best interests. We won’t sacrifice performance by making changes to their account simply to obtain a badge or certification from an ad platform, regardless of their market power.”

If you can check that box and are willing to put clients first, we’ve got a badge for you. One you can feel proud to show.

A badge that should open a healthy dialog with your clients about this topic.

We put this together with no catch, tricks, or BS.

We’ve just had enough.

This is the hill that I will die on with Google Partners.

Instead of leaving empty-handed, I’ll be sleeping well at night knowing that my agency will always put clients first.

If you’d like to sign up to be in this directory and get a free badge, head on over to and pledge your loyalty to your clients not to Google and their recommendations.

More Resources:

Image Credits

Screenshot taken by author, February 2023

Data Science Vs. Cyber Security: Which Career Is Best For You?

Qualities Need to Pursue as Career

These below mentioned fundamental qualities are needed to take up career in Data Science as well as Cyber Security.

Digital Education

While numerous industries like e-Commerce and tourism benefitted enormously from the digital transformation, education succeeded in spreading light. Books alone are not practical but need multimedia support that the internet enhances at minimal expense. Like never before, students can now learn online at their own pace and convenience without the hassles of travel and a high cost. Like grass, online education grows everywhere. Make a beginning with Cyber Security or Data Science and reach far along the career path. Which to choose is a matter of interest since they deal with the same cyber field.

Technical Backgrounds Soft skills

A well-rounded personality essential to success must include some soft skills too. Dress, communication skills, language, behavior, and manners count so much and more nowadays in an increasingly intelligent world. Critical thinking and problem-solving can look at all sides of a tricky matter. Teamwork and collaboration, along with leadership skills, should be considered crucial. Working online and remotely and teaming up with work worldwide on projects has become common.

Consider the Essentials of Cybersecurity & Data Science

If programming and architecture are attractive, data science is indicated. Cyber Security workers also deal with the management of data systems and require copious information. They do not create systems but guard systems from threats. Let’s know about the vitals of Cybersecurity and Data Science.

What all includes in Cyber Security?

Computer engineering, computer science, or information security degrees are preferable. Certifications help as CISA and CISSP. Professional roles may be cyber security analyst, network engineer, systems engineer, and cryptographer. Cyber Security workers guard data networks and systems and play the role of gatekeepers to keep out fraudsters. They prevent security breaches that are common with ransom demands and mighty losses. They formulate audit procedures to assist maintenance. They should be able to identify intrusions and respond effectively with problem-solving methods. They need to keep up to date with new trends.

What includes in Data Science?

Specialists preferably need both a bachelor’s and master’s degree in Data Science. Professionally, they may be a data analyst, data scientist, or data architect. They may be called machine learning engineers or data engineers too. Exploring the infinite data forest, they need maths and statistics and work with Python programming language and SQL, among others. Artificial Intelligence and Machine Learning techniques are further crucial areas of research. Data Science fundamentally works with massive amounts of raw data to retrieve what is essential. Such sifting finds valuable insights that help brands, products, and services. They are data analysis conductors, prescriptive analytics, and machine learning experts.

Interrelation of Cyber Security & Data Science

While cyber security protects from weaknesses, data science needs new data. It may be called a reciprocal relationship where one cannot do without the other. Cybersecurity data science seems more comprehensive when data comes from security systems; thus, the results are doubly secure. The cyber security data science idea presents more robust formats with actionable avenues than conventional approaches.

Data science has to take the help of cyber security for protection. Conversely, cyber security requires data science to guard data and their systems and networks. Developments in Artificial Intelligence and Machine Learning help cyber security.

5 fields of Cyber Security

Critical infrastructure security

Internet of Things (IoT) security

Network security

Application Security

Cloud Security

Cybersecurity Analyst’s Work Duties

Professionals design firewalls to protect networking systems. They decide who can access the systems and how. Securing the IT systems, they constantly monitor and watch for signs of danger. They identify attackers and take preventive action. They frame security policies for the organization and work with employees.

Data Scientists’ Work Duties

They confirm the quality of data and construct machine-learning algorithms. They purify unstructured data and build collaborative models for research. Presenting data through visualization is very effective. They set up and maintain databases, prepare reports and suggest solutions.

Importance of Cyber Security & Data Science Conclusion

Rather than a mighty conquest, isn’t it better to ascend the professional hierarchy in stages? According to circumstances, choose a study and training path and begin professional life. Further opportunities will arise in the years to come. The digital world has enough jobs for everybody, and the chances are growing daily. Cyber Security and Data Science signify the actionable future.

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